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Flashcard 4494295960844

Question
In AWS, lets say you created an EC2 instance that you configured a bootstrap script for (i.e. to install some packages, like httpd, when instance first starts), from inside the instance shell, issue command to see the content of that bootstrap script.
Answer

sudo curl http://169.254.169.254/latest/user-data


^^ note that instead of getting just user-data scripts with above command, you can get instance meta-data like public-ipv4 by issuing similar command: sudo curl http://169.254.169.254/latest/meta-data/public-ipv4


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The time horizon at which flu predictions are generally offered is in the order of 2 to 4 wk, which is generally too short for action.
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Reich et al. (13) present a thorough comparison of prediction skills across seasons and US regions, shedding light on how forecasting performances vary with geography. Some regions appear more difficult to predict than others (e.g., a null model based on historical averages does poorly, indicating large interannual variability in ILI incidences). In contrast, other regions are easier to predict due to greater stability in observed historical patterns and substantial improvement of predictive models over historical averages. Moving forward, it will be important to understand whether regional differences in predictive skills are a reporting artifact or whether they reflect heterogeneities in influenza transmission dynamics. Demographic and environmental differences among regions, connectivity, and spatial extent could all affect predictive skills. This question could have practical implications because regions displaying consistently high predictive power could be used as sentinels for influenza surveillance.
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A more sensible formulation of the infectious period would be to specify the probability of leaving a class as a function of the time spent within the class, such that initially the chance of leaving the class is small, but the probability increases as the mean infectious/latent period is reached.
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More realistic distributions for the length of the infectious period can be obtained by choosing p(t) to be a gamma probability density function [22–27]
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Chretien et al. (2014)'s review suggests need for:

  • use of good practices in influenza forecasting (e.g., sensitivity analysis);
  • direct comparisons of diverse approaches;
  • assessment of model calibration;
  • integration of subjective expert input;
  • operational research in pilot, real-world applications; and
  • improved mutual understanding among modelers and public health officials
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Chretien et al. (2014) included 35 publications in the review [10–44] (Figure 1). Twenty six (74%) of the studies were published in 2009 or later, with more than one-third published in 2012 or 2013. The studies fell into 3 categories based on the epidemiological application: population-based seasonal influenza forecasting (N = 27 publica- tions), medical facility-based forecasting of patient counts for seasonal or pandemic influenza (N = 4), and regional or global spread forecasting for pandemic influenza (N = 4) (Table 1). Most studies included areas of North America (N = 15) or Europe (N = 14 publications), while a few included areas in the Asia- Pacific region (N = 4) or had global scope (N = 3) (Table 1). Twenty-eight studies employed temporal forecasting without a spatial component, while 7 made forecasts in time and space (Table 1). The studies used diverse forecasting methods, with 18 using statistical approaches without models for epidemiological processes and 17 employing epidemiological models (Table 1).
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mean (or median) absolute error and mean absolute percent error are the most common metrics for forecasts of incidence (i.e., daily, weekly, or monthly incidence; peak incidence; or cumulative incidence)
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The branching process approximation is a CTMC, but near the disease-free equilibrium, the rates are linear (Table 2).

Three important assumptions underlie the branching process approximation:

  1. Each infectious individual behavior is independent from other infectious individuals. Reasonable if a small number of infectious individuals is introduced into a large homogeneously-mixed population (assumption (3)).
  2. Each infectious individual has the same probability of recovery and the same probability of transmitting an infection. Reasonable in a homogeneously-mixed population with constant transmission and recovery rates, b and g.
  3. The susceptible population is sufficiently large.
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Flashcard 4596938706188

Question
For a few initial infectious individuals, the branching process either [...] or hits zero.
Answer
grows exponentially

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For a few initial infectious individuals, the branching process either grows exponentially or hits zero.

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Flashcard 4596939754764

Question
For a few initial infectious individuals, the branching process either grows exponentially or [...].
Answer
hits zero

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For a few initial infectious individuals, the branching process either grows exponentially or hits zero.

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This paper (Osthus et al., 2019) makes contributions and advances in the following ways.

  • We introduce and demonstrate the importance of discrepancy modeling to the growing and consequential field of flu forecasting. Discrepancy modeling is done hierarchically, allowing information to be shared across available flu seasons.
  • We demonstrate the superiority of our approach relative to all models that competed in the CDC’s 2015–2016 and 2016–2017 flu forecasting challenges, providing yet another instance where discrepancy modeling is not only conceptually appealing but also practically effective.
  • In an effort to advance flu forecasting capabilities, much effort has been spent identifying possibly useful, nontraditional data sources such as Google (Ginsberg et al., 2009) and Wikipedia (Generous et al., 2014). Alternatively, as we demonstrate, flu forecasting can be improved through carefully made modeling choices, making use of the available traditional data hierarchically.
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#2018_Adalja_etal_pandemic_potential_pathogens #reading

Attributes likely to be essential components of any GCBR-level pathogen include:

  • efficient human-to-human transmissibility,
  • an appreciable case fatality rate,
  • the absence of an effective or widely available medical countermeasure,
  • an immunologically naïve population,
  • virulence factors enabling immune system evasion, and
  • respiratory mode of spread.

Additionally, the ability to transmit during incubation periods and/or the occurrence of mild illnesses would further augment spread.

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Flashcard 4607800642828

Question
In AWS, lets say you have a VPC with a private subnet and you want instances in your private subnet to talk to S3 but you don't want to configure a NAT for your private subnet. How do you achieve this (be as specific as possible, i.e there are two steps that take place that get this achieved).
Answer
A: 1) you create a VPC gateway endpoint for S3 (from the "Endpoints" sectoin of VPC console), and 2) you update your route table for your private subnet to send the traffic for S3 to this newly created VPC endpoint gateway (NOTE: the route is added for your automatically when you create the S3 Gateway Endpoint)

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#reading
“[Statistical] algorithmic invention is a free-wheeling and adventurous enterprise, with inference playing catch-up as it strives to assess the accuracy, good or bad, of some hot new algorithmic methodology.” [Efron and Hastie, 2016, pxvi]
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#reading

Typically, estimators can be divided into two types: point estimators and set estimators.

  • A point estimator which maps from the sample space X to a point in the parameter space Θ.
  • A set estimator which maps from X to a set in Θ.
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#reading
Cox and Hinkley (1974; p12) observe, if we are interested in comparing two possible values of θ using the likelihood, then we should consider the ratio of the likelihoods rather than, for example, the difference
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#reading
The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or X = y was observed.
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#reading
The strong likelihood principle: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or Y = y was observed.
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#reading
Sufficient statistic: A statistic S = s(X) is sufficient for θ if the conditional distribution of X, given the value of s(X) (and θ), fX|S(x | s, θ), does not depend upon θ.
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Flashcard 4610384071948

Question
[...] is an often used and effective modeling approach in the field of computer experiments, where systematic deviations between mechanistic models and data can be common (e.g., Kennedy and O’Hagan, 2001;Bayarri et al., 2007; Higdon et al., 2008;Brynjarsd´ottir and O’Hagan, 2014).
Answer
Discrepancy modeling

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Discrepancy modeling is an often used and effective modeling approach in the field of computer experiments, where systematic deviations between mechanistic models and data can be common (e.g., Kennedy and O’H

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#reading
a sufficient statistic S is a vector
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Note that, in general, a sufficient statistic S is a vector and that if S is sufficient then so is any one-to-one function of S.

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#reading
if S is sufficient then so is any one-to-one function of S
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Note that, in general, a sufficient statistic S is a vector and that if S is sufficient then so is any one-to-one function of S.

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#reading
Any function of a random variable X is termed a statistic.
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Definition 2 (Statistic; estimator) Any function of a random variable X is termed a statistic. If T is a statistic then T = t(X) is a random variable and t = t(x) the corresponding value of the random variable when X = x. In general, T is a vector. A statistic designed to estimat

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#reading
If T is a statistic then T = t(X) is a random variable and t = t(x) the corresponding value of the random variable when X = x.
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Definition 2 (Statistic; estimator) Any function of a random variable X is termed a statistic. If T is a statistic then T = t(X) is a random variable and t = t(x) the corresponding value of the random variable when X = x. In general, T is a vector. A statistic designed to estimate θ is termed an estimator.

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#reading
A statistic designed to estimate θ is termed an estimator.
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andom variable X is termed a statistic. If T is a statistic then T = t(X) is a random variable and t = t(x) the corresponding value of the random variable when X = x. In general, T is a vector. <span>A statistic designed to estimate θ is termed an estimator. <span>

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#reading
Typically, estimators can be divided into two types called: point estimators and set estimators.
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Typically, estimators can be divided into two types: point estimators and set estimators. A point estimator which maps from the sample space X to a point in the parameter space Θ. A set estimator which maps from X to a set in Θ.

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#reading
Point estimators map from the sample space X to a point in the parameter space Θ.
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Typically, estimators can be divided into two types: point estimators and set estimators. A point estimator which maps from the sample space X to a point in the parameter space Θ. A set estimator which maps from X to a set in Θ.

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#reading
Set estimators map from the sample space X to a set in Θ.
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span> Typically, estimators can be divided into two types: point estimators and set estimators. A point estimator which maps from the sample space X to a point in the parameter space Θ. A set estimator which maps from X to a set in Θ. <span>

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Flashcard 4610409237772

Tags
#reading
Question
The likelihood for θ given observations x is LX(θ; x) = [...]
Answer
fX(x |θ)

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Definition 3 (Likelihood) The likelihood for θ given observations x is LX(θ; x) = fX(x |θ), θ ∈ Θ regarded as a function of θ for fixed x.

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#reading
To compare two parameters based on likelihood, we should consider the ratio of the likelihoods.
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Cox and Hinkley (1974; p12) observe, if we are interested in comparing two possible values of θ, θ1 and θ2 say, using the likelihood then we should consider the ratio of the likelihoods rather than, for example, the difference

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Flashcard 4610415267084

Tags
#reading
Question
To compare two parameters based on likelihood, we should consider the [...] of the likelihoods.
Answer
ratio

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To compare two parameters based on likelihood, we should consider the ratio of the likelihoods.

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Flashcard 4610418937100

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#reading
Question
[...] observe, if we are interested in comparing two possible values of θ, θ1 and θ2 say, using the likelihood then we should consider the ratio of the likelihoods rather than, for example, the difference
Answer
Cox and Hinkley (1974; p12)

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Cox and Hinkley (1974; p12) observe, if we are interested in comparing two possible values of θ, θ1 and θ2 say, using the likelihood then we should consider the ratio of the likelihoods rather than, for example, t

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Flashcard 4610423131404

Tags
#reading
Question
The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that [...] then the inference about θ should be the same irrespective of whether X = x or X = y was observed.
Answer
LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ

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The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or X = y was observed.

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Flashcard 4610424179980

Tags
#reading
Question
[...]: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or X = y was observed.
Answer
The weak likelihood principle

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The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irresp

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Flashcard 4610425228556

Tags
#reading
Question
The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then [...]
Answer
the inference about θ should be the same irrespective of whether X = x or X = y was observed.

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The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or X = y was observed.

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Flashcard 4610428898572

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#reading
Question
[...]: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or Y = y was observed.
Answer
The strong likelihood principle

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The strong likelihood principle: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ

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Flashcard 4610432830732

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#reading
Question
The strong likelihood principle: Let EX and EY be two experiments which [...]. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or Y = y was observed.
Answer
have the same parameter θ

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The strong likelihood principle: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or Y = y was obs

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Flashcard 4610433879308

Tags
#reading
Question
The strong likelihood principle: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that [...] then the inference about θ should be the same irrespective of whether X = x or Y = y was observed.
Answer
LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ

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The strong likelihood principle: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or Y = y was observed.

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Flashcard 4610434927884

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#reading
Question
The strong likelihood principle: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then [...]
Answer
the inference about θ should be the same irrespective of whether X = x or Y = y was observed.

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e strong likelihood principle: Let EX and EY be two experiments which have the same parameter θ. If X = x and Y = y are two observations such that LY (θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then <span>the inference about θ should be the same irrespective of whether X = x or Y = y was observed. <span>

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Flashcard 4610440170764

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#reading
Question
[...]: A statistic S = s(X) where the conditional distribution of X, given the value of s(X) (and θ), fX|S(x | s, θ), does not depend upon θ.
Answer
Sufficient statistic

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Sufficient statistic: A statistic S = s(X) is sufficient for θ if the conditional distribution of X, given the value of s(X) (and θ), fX|S(x | s, θ), does not depend upon θ.

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Flashcard 4610441219340

Tags
#reading
Question
Sufficient statistic: A statistic S = s(X) is sufficient for θ if [...]
Answer
the conditional distribution of X, given the value of s(X) (and θ), fX|S(x | s, θ), does not depend upon θ.

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Sufficient statistic: A statistic S = s(X) is sufficient for θ if the conditional distribution of X, given the value of s(X) (and θ), fX|S(x | s, θ), does not depend upon θ.

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#reading
It should be clear from the definition of a sufficient statistic that the sufficiency of S for θ is dependent upon the choice of the family of distributions in the model.
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#reading
Sufficiency for a parameter θ can be viewed as the idea that S captures all of the information about θ contained in X. Having observed S, nothing further can be learnt about θ by observing X as fX|S(x | s, θ) has no dependence on θ.
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#reading
Fisher-Neyman Factorization Theorem: The statistic S = s(X) is sufficient for θ if and only if, for all x and θ, fX(x |θ) = g(s(x), θ)h(x) for some pair of functions g(s(x), θ) and h(x).
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#reading
The sufficiency principle: If S = s(X) is a sufficient statistic for θ, and x and y are two observations such that s(x) = s(y), then the inference about θ should be the same irrespective of whether X = x or Y = y was observed.
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#reading

Following Section 2.2(iii) of Cox and Hinkley (1974), we may interpret sufficiency as follows.

  1. Consider two individuals who both assert the model E = {X, Θ, fX(x |θ)}.
  2. The first individual observes x directly.
  3. The second individual also observes x but in a two stage process:
    1. They first observe a value s(x) of a sufficient statistic S with distribution fS(s |θ).
    2. They then observe the value x of the random variable X with distribution fX|S(x |s) which does not depend upon θ.

It may well then be reasonable to argue that, as the final distribution for X for the two individuals are identical, the conclusions drawn from the observation of a given x should be identical for the two individuals. That is, they should make the same inference about θ.

For the second individual, when sampling from fX|S(x |s) they are sampling from a fixed distribution and so, assuming the correctness of the model, only the first stage is informative: all of the knowledge about θ is contained in s(x).

If one takes these two statements together then the inference to be made about θ depends only on the value s(x) and not the individual values x i contained in x.

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#reading
There are two broad approaches to statistical inference, generally termed the classical (or frequentist) approach and the Bayesian approach.
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#reading
In classical statistics, the parameter is viewed as a fixed unknown constant and inferences are made utilising the distribution fX(x |θ) even after the data x has been observed. Conversely, in a Bayesian approach parameters are treated as random and so may be equipped with a probability distribution.
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#reading
In a classical approach to statistical inference, no further probabilistic assumptions are made once the parametric model E = {X, Θ, fX(x |θ)} is specified. In particular, θ is treated as an unknown constant and interest centres on constructing good methods of inference
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#reading
Intuitively, the MLE is a reasonable choice for an estimator: it’s the value of θ which makes the observed sample most likely
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#reading
The MLE satisfies the invariance property [Theorem 7.2.10, Casella and Berger (2002)] that if is the MLE of θ then for any function g(θ), the MLE of g(θ) is g().
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#reading

Hastie and Efron (2016) consider that there are three ages of statistical inference:

  1. the pre-computer age (essentially the period from 1763 and the publication of Bayes’ rule up until the 1950s),
  2. the early-computer age (from the 1950s to the 1990s),
  3. and the current age (a period of computer-dependence with enormously ambitious algorithms and model complexity).

With these developments in mind, it is clear that there exist a hierarchy of statistical models.

  1. Models where fX(x |θ) has a known analytic form.
  2. Models where fX(x |θ) can be evaluated.
  3. Models where we can simulate X from fX(x |θ).

Between the first case and the second case exist models where fX(x |θ) can be evaluated up to an unknown constant, which may or may not depend upon θ. In the first case, we might be able to derive an analytic expression for θ or to prove that fX(x |θ) has a unique maximum so that any numerical maximisation will converge to θ(x).

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#reading

The choice of algorithm is critical: the MLE is a good method of inference only if:

  • you can prove that it has good properties for your choice of fX(x |θ) and
  • you can prove that the algorithm you use to find the MLE of fX(x |θ) does indeed do this.

The second point arises once the choice of estimator has made.

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#reading
As estimator T = t(X) is said to be unbiased if bias(T | θ) = E(T | θ) − θ is zero for all θ ∈ Θ. This is a superficially attractive criterion but it can lead to unexpected results (which are not sensible estimators) even in simple cases.
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Flashcard 4610472938764

Tags
#reading
Question
As estimator T = t(X) is said to be unbiased if [...]. This is a superficially attractive criterion but it can lead to unexpected results (which are not sensible estimators) even in simple cases.
Answer
bias(T | θ) = E(T | θ) − θ is zero for all θ ∈ Θ

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As estimator T = t(X) is said to be unbiased if bias(T | θ) = E(T | θ) − θ is zero for all θ ∈ Θ. This is a superficially attractive criterion but it can lead to unexpected results (which are not sensible estimators) even in simple cases.

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#reading
bias(T | θ) = E(T | θ) − θ
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As estimator T = t(X) is said to be unbiased if bias(T | θ) = E(T | θ) − θ is zero for all θ ∈ Θ. This is a superficially attractive criterion but it can lead to unexpected results (which are not sensible estimators) even in simple cases.

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Flashcard 4610475560204

Tags
#reading
Question
bias(T | θ) = [...]
Answer
E(T | θ) − θ

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bias(T | θ) = E(T | θ) − θ

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#reading
A drawback with the bias is that it is not, in general, transformation invariant. For example, if T is an unbiased estimator of θ then T−1 is not, in general, an unbiased estimator of θ−1 as E(T−1 | θ) 1/E(T | θ) = θ−1 .
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#reading
For an estimator T, a better criterion to being unbiased is that T has small mean square error (MSE)
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#reading
MSE(T | θ) = Var(T | θ) + bias(T | θ)2 .
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Flashcard 4610485259532

Tags
#reading
Question
[...] = Var(T | θ) + bias(T | θ)2 .
Answer
MSE(T | θ)

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MSE(T | θ) = Var(T | θ) + bias(T | θ)2 .

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Flashcard 4610486832396

Tags
#reading
Question
MSE(T | θ) = [...] + bias(T | θ)2 .
Answer
Var(T | θ)

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MSE(T | θ) = Var(T | θ) + bias(T | θ)2 .

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Flashcard 4610487880972

Tags
#reading
Question
MSE(T | θ) = Var(T | θ) + [...] .
Answer
bias(T | θ)2

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MSE(T | θ) = Var(T | θ) + bias(T | θ)2 .

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#reading
it is properties of the distribution of the estimator T, known as the sampling distribution, across the range of possible values of θ that are used to determine whether or not T is a good inference rule
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#reading
The assessment of whether T is a good estimator is made not for the observed data x but based on the distributional properties of X
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#reading
a key principle of the classical approach that 1. Every algorithm is certified by its sampling distribution, and 2. The choice of algorithm depends on this certification.
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#reading
If we accept, as our working hypothesis, that one of the elements in the family of distributions is true (ie: that there is a θ∗ ∈ Θ which is the true value of θ) then the corresponding predictive distribution fY |X(y |x, θ∗ ) is the true predictive distribution for Y . The classical solution is to replace θ∗ by plugging-in an estimate based on x.
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#reading
In a Bayesian approach to statistical inference, we consider that, in addition to the parametric model E = {X, Θ, f X (x |θ)}, the uncertainty about the parameter θ prior to observing X can be represented by a prior distribution π on θ.
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#reading
O’Hagan and Forster (2004; p5) note, “the posterior distribution encapsulates all that is known about θ following the observation of the data x, and can be thought of as comprising an all-embracing inference statement about θ.”
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#reading
In contrast to the plug-in classical approach to prediction, the Bayesian approach can be viewed as integrate-out. If E B = {X × Y, Θ, f X,Y (x, y |θ), π(θ)} is our Bayesian model for (X, Y ) and we are interested in prediction for Y given X = x then we can integrate out θ to obtain the parameter free conditional distribution f Y |X (y |x): f Y |X (y |x) = Z Θ f Y |X (y |x, θ)π(θ |x) dθ.
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#reading
Whilst the posterior distribution expresses all of knowledge about the parameter θ given the data x, in order to express this knowledge in clear and easily understood terms we need to derive appropriate summaries of the posterior distribution. Typical summaries include point estimates, interval estimates, probabilities of specified hypotheses.
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#reading
E(θ |X), the posterior expectation, minimises the posterior expected square error and the minimum value of this error is Var(θ |X), the posterior variance.
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Disease forecasts in the order of 2 mo or more would be particularly useful to ramp up interventions and adjust hospital surge capacity.
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the 4-wk time horizon, beyond which the average of historical incidences (i.e., a null model) becomes the most reliable option for prediction. From a public health standpoint, a longer horizon, <span>in the order of 2 mo or more, would be particularly useful to ramp up interventions and adjust hospital surge capacity. Ideally, even longer timescales should be considered so that the prediction of epidemic intensity (epidemic size) and severity (total number of hospitalizations and deaths) aligns with

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Forecasts with longer timescales should be considered so that the prediction of epidemic intensity (epidemic size) and severity (total number of hospitalizations and deaths) aligns with the vaccine manufacturing process.
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diction. From a public health standpoint, a longer horizon, in the order of 2 mo or more, would be particularly useful to ramp up interventions and adjust hospital surge capacity. Ideally, even <span>longer timescales should be considered so that the prediction of epidemic intensity (epidemic size) and severity (total number of hospitalizations and deaths) aligns with the vaccine manufacturing process. <span>

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Flashcard 4610510425356

Tags
#2018_Adalja_etal_pandemic_potential_pathogens #reading
Question
microbiologically specific diagnoses of infectious disease syndromes in strategic or sentinel locations around the world should become more routine, especially now that [...]
Answer
diagnostics are becoming more powerful and available

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microbiologically specific diagnoses of infectious disease syndromes in strategic or sentinel locations around the world should become more routine, especially now that diagnostics are becoming more powerful and available

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Osthus et al. (2019) perform discrepancy modeling hierarchically, allowing information to be shared across available flu seasons.
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t al., 2019) makes contributions and advances in the following ways. We introduce and demonstrate the importance of discrepancy modeling to the growing and consequential field of flu forecasting. <span>Discrepancy modeling is done hierarchically, allowing information to be shared across available flu seasons. We demonstrate the superiority of our approach relative to all models that competed in the CDC’s 2015–2016 and 2016–2017 flu forecasting challenges, providing yet another instance where

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SET vs LET variables

SET assigns the statement literally, LET assigns the result after executing the statement

Statement Value of vVariable

SET v Variable = 1 + 2; 1 + 2

LET vVariable = 1 + 2; 3

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DUAL data type
DUAL data type Besides the usual data types, QlikView has a data type that can be interpreted as both a number and a string—the DUAL data type. This data type is often used for months, where a month field may return both an abbreviation (Jun) and a number (6). Dual values are created using the Dual() function. For example: Dual('June', 6)
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DateTime data type is a floating point
It is important to understand that, underneath, the DateTime data type is represented by a floating point number. For example, 12 noon on May 22nd 2012 is stored as 41,051.5. The whole number 41,051 represents the date; it is the number of days that have passed since December 31st, 1899. The fractional part 0.5 represents the time. As a day (24 hours) is 1, an hour is 1/24 and 12 hours is 12/24, which is equal to 1/2 or 0.5. Knowing this, we can use many of the numeric functions that we saw earlier to perform date and time calculations. For example, we can use the Floor()function to remove the time information from a date.
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A companhia aberta é obrigada a apresentar a DVA. Os órgãos reguladores (ANTT, BACEN, PREVIC, ANATEL, etc), no entanto, podem exigir que as empresas a ele submetidas apresentem a DVA. Nos demais casos, a apresentação dessa demonstração é facultativa. A Resolução CFC 1.162/2009 afirma que "É recomendado, entretanto, a sua elaboração por todas as entidades que divulgam demonstrações contábeis."
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Conjunto completo de demonstrações contábeis 10. O conjunto completo de demonstrações contábeis inclui: (a) balanço patrimonial ao final do período; (b) demonstração do resultado do período; (ba) demonstração do resultado abrangente do período; (c) demonstração das mutações do patrimônio líquido do período; (d) demonstração dos fluxos de caixa do período; (da) demonstração do valor adicionado do período, conforme NBC TG 09 – Demonstração do Valor Adicionado, se exigido legalmente ou por algum órgão regulador ou mesmo se apresentada voluntariamente; (e) notas explicativas, compreendendo as políticas contábeis significativas e outras informações elucidativas; (ea) informações comparativas com o período anterior, conforme especificado nos itens 38 e 38A; (f) balanço patrimonial do início do período mais antigo, comparativamente apresentado, quando a entidade aplica uma política contábil retrospectivamente ou procede à reapresentação retrospectiva de itens das demonstrações contábeis, ou quando procede à reclassificação de itens de suas demonstrações contábeis de acordo com os itens 40A a 40D.
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Date and Time functions
#has-images
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String Functions, p1
#has-images
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O Balanço Patrimonial representa uma posição estática, compreendendo todos os bens e direitos (Ativo), as obrigações (Passivo Exigível) e o Patrimônio Líquido da entidade em uma determinada data.
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A estrutura e a classificação das contas encontram-se disciplinadas nos artigos 178 a 184 da Lei 6.404/76. Segundo o caput do art. 178, “(...) as contas serão classificadas segundo os elementos do patrimônio que registrem, e agrupadas de modo a facilitar o conhecimento e a análise da situação financeira da companhia”.
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String functions, p2
#has-images
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String functions, p3
#has-images
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Art. 178, § 1º No ativo, as contas serão dispostas em ordem decrescente de grau de liquidez dos elementos nelas registrados, nos seguintes grupos: I – ativo circulante; e II – ativo não circulante, composto por ativo realizável a longo prazo, investimentos, imobilizado e intangível. § 2º No passivo, as contas serão classificadas nos seguintes grupos: I – passivo circulante; II – passivo não circulante; e
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III – patrimônio líquido, dividido em capital social, reservas de capital, ajustes de avaliação patrimonial, reservas de lucros, ações em tesouraria e prejuízos acumulados. § 3º Os saldos devedores e credores que a companhia não tiver direito de compensar serão classificados separadamente. Em que pese a Lei não normatizar expressamente, no passivo as contas serão dispostas em ordem decrescente de grau de exigibilidade dos elementos nela registrados. Portanto, as obrigações vincendas até o término do exercício seguinte serão classificadas no Passivo Circulante. As obrigações vincendas após o término do exercício seguinte serão classificadas no Passivo Não Circulante.
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O “Grau de Liquidez” está associado ao prazo no qual os bens e direitos podem ser transformados em dinheiro. Sendo assim, quanto menor o prazo, maior a liquidez e vice-versa.
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O “Grau de Exigibilidade”, assim como o de Liquidez, está associado ao prazo no qual as obrigações devem ser pagas. Portanto, quanto menor o prazo maior o grau de exigibilidade e vice-versa.
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ativo é um recurso controlado pela entidade como resultado de eventos passados e do qual se espera que resultem futuros benefícios econômicos para a entidade. Estudamos, ainda, que o direito de propriedade não é essencial à existência do ativo!
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Ativo Circulante (AC) Conforme vimos no balanço acima, o AC é composto pelos bens e direitos de curto prazo, assim considerados aqueles que a companhia espere que sejam realizados até doze meses após a data do Balanço Patrimonial. Trata-se do Capital de Giro da entidade, pois representa os bens e direitos em constante circulação.
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Portanto, no AC serão classificados: ▪ As disponibilidades; ▪ Os direitos realizáveis no curso do exercício social subsequente; ▪ As aplicações de recursos em despesas do exercício seguinte.
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Na empresa em que o ciclo operacional tiver duração maior que o exercício social, a classificação no circulante terá por base o prazo desse ciclo (art. 179, parágrafo único)
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Ativo Não Circulante - Realizável a Longo Prazo (ARLP) De acordo com o art. 179 da Lei 6.404/76, Art. 179. As contas serão classificadas do seguinte modo: II - no ativo realizável a longo prazo: os direitos realizáveis após o término do exercício seguinte, assim como os derivados de vendas, adiantamentos ou empréstimos a sociedades coligadas ou controladas (artigo 243), diretores, acionistas ou participantes no lucro da companhia, que não constituírem negócios usuais na exploração do objeto da companhia;
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Portanto, no ARLP serão classificados: ▪ Direitos realizáveis após o término do exercício seguinte; ▪ Direitos com pessoas ligadas de negócio não usual.
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Para que um direito seja classificado no ARLP, independentemente do prazo, deve atender três requisitos cumulativamente, quais sejam: 1º) Os direitos devem ser decorrentes de vendas, adiantamentos ou empréstimos; 2º) Os direitos devem estar relacionados com controladas, coligadas, diretores, acionistas ou participantes no lucro; 3º) A operação deve ser não usual.
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Para o ARLP vale o mesmo destaque que vimos no Ativo Circulante: Na companhia em que o ciclo operacional tiver duração maior que o exercício social, a classificação no Realizável a Longo Prazo terá por base o prazo desse ciclo (art. 179, parágrafo único)
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Vale lembrar que os empréstimos a controladas, coligadas, diretores, acionistas e participantes no lucro, independente do prazo, serão classificados no ARLP
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E se o ciclo operacional for menor que o exercício social? O que acontece se o ciclo operacional for de 10 meses, por exemplo? Nesse caso classificamos como circulante com base no exercício social (12 meses).
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Ativo Não Circulante - Investimentos De acordo com o art. 179 da Lei 6.404/76, Art. 179. As contas serão classificadas do seguinte modo: III - em investimentos: as participações permanentes em outras sociedades e os direitos de qualquer natureza, não classificáveis no ativo circulante, e que não se destinem à manutenção da atividade da companhia ou da empresa;
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Os imóveis para aluguel podem ser classificados tanto no imobilizado como em investimentos, a depender da finalidade desse aluguel. Será classificado no imobilizado quando o aluguel estiver relacionado às atividades fins da empresa (veja exemplo de questão exigida nesse sentido abaixo). Caso o aluguel não esteja relacionado com as atividades da empresa classificamos como investimentos (propriedade para investimento).
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De acordo com o art. 179 da Lei 6.404/76, Art. 179. As contas serão classificadas do seguinte modo: IV – no ativo imobilizado: os direitos que tenham por objeto bens corpóreos destinados à manutenção das atividades da companhia ou da empresa ou exercidos com essa finalidade, inclusive os decorrentes de operações que transfiram à companhia os benefícios, riscos e controle desses bens;
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Segundo o CPC 27 – Ativo Imobilizado, Ativo imobilizado é o item tangível que: (a) é mantido para uso na produção ou fornecimento de mercadorias ou serviços, para aluguel a outros, ou para fins administrativos; e (b) se espera utilizar por mais de um período.
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De acordo com o art. 179 da Lei 6.404/76, Art. 179. As contas serão classificadas do seguinte modo: VI – no intangível: os direitos que tenham por objeto bens incorpóreos destinados à manutenção da companhia ou exercidos com essa finalidade, inclusive o fundo de comércio adquirido.
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O CPC 04 (item 8) define Ativo Intangível como sendo um Ativo não monetário 3 , identificável, sem substância física. Perceba que o Intangível abrange as contas representativas dos recursos aplicados em bens imateriais (incorpóreos).
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Por fim, vale destacar que, conforme exigência prevista no CPC 04, um ativo somente poderá ser enquadrado na definição de intangível quando cumulativamente: ▪ seja identificável; ▪ seja controlável; e ▪ seja gerador de benefícios futuros. Um ativo satisfaz o critério de identificação, em termos de definição de um ativo intangível, quando:
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a) for separável, ou seja, puder ser separado da entidade e vendido, transferido, licenciado, alugado ou trocado, individualmente ou junto com um contrato, ativo ou passivo relacionado, independente da intenção de uso pela entidade; OU (os requisitos não são cumulativos!) b) resultar de direitos contratuais ou outros direitos legais, independentemente de tais direitos serem transferíveis ou separáveis da entidade ou de outros direitos e obrigações. O CPC 04 destaca que caso um item não atenda à definição de ativo intangível, o gasto incorrido na sua aquisição ou geração interna deve ser reconhecido como despesa quando incorrido. A definição de ativo intangível requer que ele seja identificável, para diferenciá-lo do ágio derivado da expectativa de rentabilidade futura gerado internamente (goodwill).
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Hiding table fields from end-users
Hiding fields Key fields can cause confusion in the QlikView frontend. As these fields are used in multiple tables, they can return unexpected results when used in an aggregation function. It is therefore advisable to hide these fields from the frontend view. There are two variables that can be used to hide fields: HidePrefix and HideSuffix. The first variable hides all field names that start with a specific text string and the second one hides all field names that end with a specific text string. To hide our key fields, we can add the following statement at the start of our script: SET HidePrefix='%';
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Subroutines
A subroutine is a reusable block of script that can be called from other places in the QlikView script by using the CALL statement. This block is formed using the SUB and END SUB control statements. Subroutines can contain parameters so that processing can be done in a flexible manner. As the QlikView script is processed in sequential order, the subroutine has to be defined before it can be called. Therefore, it is advisable to create subroutines as early as possible in the script. When executing the script, everything between the SUB and END SUB control statements is ignored by QlikView. The subroutine is only run when it is called via the CALL statement.
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Flashcard 4610860649740

Question
For the same basic reproductive ratio and average infectious period larger values of the Gamma distribution parameter n lead to a [...] in prevalence and an epidemic of shorter duration.
Answer
steeper increase

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For the same basic reproductive ratio and average infectious period larger values of the Gamma distribution parameter n lead to a steeper increase in prevalence and an epidemic of shorter duration.

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Flashcard 4610862222604

Question
For the same basic reproductive ratio and average infectious period, larger values of the Gamma distribution parameter n lead to a steeper increase in prevalence and an epidemic of [...] duration.
Answer
shorter

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For the same basic reproductive ratio and average infectious period larger values of the Gamma distribution parameter n lead to a steeper increase in prevalence and an epidemic of shorter duration.

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Flashcard 4610864319756

Question
Reich et al. (2019)'s results should not be used to extrapolate hypothetical accuracy in pandemic settings, as [...]
Answer
these models were optimized specifically to forecast seasonal influenza

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Reich et al. (2019)'s results should not be used to extrapolate hypothetical accuracy in pandemic settings, as these models were optimized specifically to forecast seasonal influenza

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Flashcard 4610866941196

Question
[...] and mean absolute percent error are the most common metrics for forecasts of incidence (i.e., daily, weekly, or monthly incidence; peak incidence; or cumulative incidence)
Answer
mean (or median) absolute error

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mean (or median) absolute error and mean absolute percent error are the most common metrics for forecasts of incidence (i.e., daily, weekly, or monthly incidence; peak incidence; or cumulative incidence)

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Flashcard 4610868514060

Question
mean (or median) absolute error and [...] are the most common metrics for forecasts of incidence (i.e., daily, weekly, or monthly incidence; peak incidence; or cumulative incidence)
Answer
mean absolute percent error

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mean (or median) absolute error and mean absolute percent error are the most common metrics for forecasts of incidence (i.e., daily, weekly, or monthly incidence; peak incidence; or cumulative incidence)

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Flashcard 4610871135500

Question
More realistic distributions for the length of the infectious period can be obtained by choosing p(t) to be a [...] probability density function [22–27]
Answer
Gamma

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More realistic distributions for the length of the infectious period can be obtained by choosing p(t) to be a gamma probability density function [22–27]

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The branching process approximation is a CTMC, but near the disease-free equilibrium, the rates are linear (Table 2).
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The branching process approximation is a CTMC, but near the disease-free equilibrium, the rates are linear (Table 2). Three important assumptions underlie the branching process approximation: Each infectious individual behavior is independent from other infectious individuals. Each infectious individua

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Three important assumptions underlie the branching process approximation:

  1. Each infectious individual behavior is independent from other infectious individuals.
  2. Each infectious individual has the same probability of recovery and the same probability of transmitting an infection.
  3. The susceptible population is sufficiently large.

Assumption (1) is reasonable if a small number of infectious individuals is introduced into a large homogeneously-mixed population (assumption (3)). Assumption (2) is also reasonable in a homogeneously-mixed population with constant transmission and recovery rates, b and g .

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The branching process approximation is a CTMC, but near the disease-free equilibrium, the rates are linear (Table 2). Three important assumptions underlie the branching process approximation: Each infectious individual behavior is independent from other infectious individuals. Each infectious individual has the same probability of recovery and the same probability of transmitting an infection. The susceptible population is sufficiently large. Assumption (1) is reasonable if a small number of infectious individuals is introduced into a large homogeneously-mixed population (assumption (3)). Assumption (2) is also reasonable in a homogeneously-mixed population with constant transmission and recovery rates, b and g .

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Moving forward, it will be important to understand whether regional differences in predictive skills are a reporting artifact or whether they reflect heterogeneities in influenza transmission dynamics. Demographic and environmental differences among regions, connectivity, and spatial extent could all affect predictive skills. This question could have practical implications because regions displaying consistently high predictive power could be used as sentinels for influenza surveillance.
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incidences). In contrast, other regions are easier to predict due to greater stability in observed historical patterns and substantial improvement of predictive models over historical averages. <span>Moving forward, it will be important to understand whether regional differences in predictive skills are a reporting artifact or whether they reflect heterogeneities in influenza transmission dynamics. Demographic and environmental differences among regions, connectivity, and spatial extent could all affect predictive skills. This question could have practical implications because regions displaying consistently high predictive power could be used as sentinels for influenza surveillance. <span>

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Qlikview Script Library: QVC
QlikView Components Instead of creating your own library of scripts, you may also want to consider QlikView Components (Qvc). Qvc is a free, open source script library. Its mission is to implement scripting best practices, improve the speed and quality of script development, and create a common ground between script developers. Qvc contains subroutines and functions to automate tasks of intermediate complexity, such as creating calendars, incremental loads, and the creation of link tables to support multiple fact tables. Qvc can be downloaded from http://code.google.com/p/ qlikview-components/
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Cabe destacar que no Passivo Não Circulante são classificadas as Receitas Diferidas (receitas recebidas antecipadamente, por exemplo, aluguéis antecipados)
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– patrimônio líquido, dividido em capital social, reservas de capital, ajustes de avaliação patrimonial, reservas de lucros, ações em tesouraria e prejuízos acumulados.
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O Manual FIPECAFI destaca que além dos itens previstos na Lei n. 6.404/76, o grupo Patrimônio Líquido pode apresentar outras contas para melhor evidenciar a situação patrimonial da companhia, bem como para atender a outras normatizações que estabeleçam a necessidade da divulgação. São exemplos de outras contas que podem ser encontradas no Patrimônio Líquido: Opções Outorgadas Reconhecidas, Gastos na Emissão de Ações, Ajustes Acumulados de Conversão, assim como contas extintas, mas possuidoras de saldos remanescentes (Reservas de Reavaliação e Reservas de Capital: Prêmio na Emissão de Debêntures e Doações e Subvenções para Investimentos).
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Capital Social Segundo o art. 182, da Lei 6.404/76, a conta do capital social discriminará o montante subscrito e, por dedução, a parcela ainda não realizada. O
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O capital social poderá ser formado com contribuições em dinheiro ou em qualquer espécie de bens suscetíveis de avaliação em dinheiro (art. 7º da Lei 6.404/76).
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Cabe destacar que, nos termos do art. 80 da Lei 6.404/76, a constituição da companhia depende do cumprimento dos seguintes requisitos preliminares: (i) Subscrição, pelo menos por 2 (duas) pessoas, de todas as ações em que se divide o capital social fixado no estatuto;
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Reservas de Capital As Reservas de Capital são valores recebidos pela empresa de sócios ou terceiros que não transitam pelo resultado do exercício. São contabilizadas diretamente no PL, ficando acumuladas para utilização posterior.
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São classificadas como reservas de capital as contas que registrarem (art. 182, Lei n. 6.404/76): ▪ a contribuição do subscritor de ações que ultrapassar o valor nominal e a parte do preço de emissão das ações sem valor nominal que ultrapassar a importância destinada à formação do capital social, inclusive nos casos de conversão em ações de debêntures ou partes beneficiárias; ▪ o produto da alienação de partes beneficiárias e bônus de subscrição;
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Para finalizar as reservas de capital, vale destacar a seguinte alteração na Lei n. 6.404/76: As doações e subvenções para investimento e os prêmios na emissão de debêntures não são mais classificados como reservas de capital. Atualmente, devemos registrar como receita do exercício.
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Ajustes de Avaliação Patrimonial Segundo o art. 182, §3º da Lei 6.404/76, serão classificadas como ajustes de avaliação patrimonial, enquanto não computadas no resultado do exercício em obediência ao regime de competência, as contrapartidas de aumentos ou diminuições de valor atribuídos a elementos do ativo e do passivo, em decorrência da sua avaliação a valor justo.
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Os ajustes de avaliação patrimonial têm como contrapartida o ajuste de instrumentos financeiros disponíveis para venda e os valores de mercado dos Ativos e Passivos nas reorganizações societárias (incorporação, fusão e Cisão).
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