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Damit etwas neues entstehen, damit Fortschritt stattfinden und neuer Wohlstand geschaffen werden kann, muss eine Unbestimmtheit herrschen, die Raum für die Entfaltung des freien Willens und der Fantasie schafft. Nur ein Zusammenprall gegensätzlicher Kräfte kann diesen fruchtbaren Nährboden erzeugen.

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In der Wertehierarchie der Organisation stehen die Kunden an oberster Stelle, an zweiter Stel- le sind die Mitarbeiter lokalisiert, und ganz unten findet sich die Organisationsleitung wieder. Zugespitzt und auf den Führungs- und Management-Kontext angewendet lautet die „Demutsfrage“: Wem oder was messe ich mehr Bedeutung bei

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Relationships among probability distributions - Wikipedia
) relationships 5 Compound (or Bayesian) relationships 6 See also 7 References 8 External links Special case of distribution parametrization[edit source] A binomial (n, p) random variable with n = 1, is a Bernoulli (p) random variable. <span>A negative binomial distribution with n = 1 is a geometric distribution. A gamma distribution with shape parameter α = 1 and scale parameter β is an exponential (β) distribution. A gamma (α, β) random variable with α = ν/2 and β = 2, is a chi-squared random




#exponential-family
The exponential family of distributions provides a general framework for selecting a possible alternative parameterisation of the distribution, in terms of natural parameters, and for defining useful sample statistics, called the natural sufficient statistics of the family.

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Exponential family - Wikipedia
ions to consider. The concept of exponential families is credited to [1] E. J. G. Pitman, [2] G. Darmois, [3] and B. O. Koopman [4] in 1935–36. The term exponential class is sometimes used in place of "exponential family". [5] <span>The exponential family of distributions provides a general framework for selecting a possible alternative parameterisation of the distribution, in terms of natural parameters, and for defining useful sample statistics, called the natural sufficient statistics of the family. Contents [hide] 1 Definition 1.1 Examples of exponential family distributions 1.2 Scalar parameter 1.3 Factorization of the variables involved 1.4 Vector parameter 1.5 Vect




Flashcard 1729339198732

Answer
n = 1


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A negative binomial distribution with n = 1 is a geometric distribution.

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Relationships among probability distributions - Wikipedia
) relationships 5 Compound (or Bayesian) relationships 6 See also 7 References 8 External links Special case of distribution parametrization[edit source] A binomial (n, p) random variable with n = 1, is a Bernoulli (p) random variable. <span>A negative binomial distribution with n = 1 is a geometric distribution. A gamma distribution with shape parameter α = 1 and scale parameter β is an exponential (β) distribution. A gamma (α, β) random variable with α = ν/2 and β = 2, is a chi-squared random







The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data

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Je höher wir unter den Führern hinaufsteigen, je notwendiger wird es, daß der Kühnheit ein überlegender Geist zur Seite trete, daß sie nicht zwecklos, nicht ein blinder Stoß der Leiden- schaft sei; denn immer weniger betrifft es die eigene Aufopferung, immer mehr knüpft sich die Haltung anderer und die Wohlfahrt eines großen Ganzen daran.

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As discussed later, probabilistic programming offers an elegant way of generalizing graphical models, allowing a much richer representation of models.

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Flashcard 1729359645964

Tags
#Borges
Question
Lo que yo encuentro sobre todo malo en los deportes es la idea de que alguien [...] y de que alguien [...] , y de que este hecho suscite [...] .
Answer
gane, pierda, rivalidades


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Borges, Jorge Luis sur Twitter : &quot;Lo que yo encuentro sobre todo malo en los deportes es la idea de que alguien gane y de que alguien pierda, y de que este hecho suscite riva… https://t.co/3bP06RPSjB&quot;
Unblock @BorgesJorgeL Report Tweet Add to other Moment Add to new Moment <span>Lo que yo encuentro sobre todo malo en los deportes es la idea de que alguien gane y de que alguien pierda, y de que este hecho suscite rivalidades. Translate from Spanish Translated from Spanish by Bing What I find especially bad at sports is the idea that someone wins an







Neothenie, ist ein Begriff aus der Biologie, die den Erhalt spezifisch jugendlicher Merkmale in späteren Entwicklungsphasen bezeichnet.

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Wenn man gut durch geöffnete Türen kommen will, muß man die Tatsache achten, daß sie einen festen Rahmen haben: dieser Grundsatz[...] ist einfach eine Forderung des Wirklichkeitssinns. Wenn es aber Wirklichkeitssinn gibt, und niemand wird bezweifeln, daß er seine Daseinsberechtigung hat, dann muß es auch etwas geben, das man Möglichkeitssinn nennen kann. Wer ihn besitzt, sagt beispielsweise nicht: Hier ist dies oder das geschehen, wird ge- schehen, muß geschehen; sondern er erfindet: Hier könnte, sollte oder müßte geschehn; und wenn man ihm von irgend etwas erklärt, daß es so sei, wie es sei, dann denkt er: Nun, es könnte wahrscheinlich auch anders sein. So ließe sich der Möglichkeitssinn geradezu als die Fähig- keit definieren, alles, was ebensogut sein könnte, zu denken und das, was ist, nicht wichtiger zu nehmen als das, wasnichtist.

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Ein Mensch mit „Möglichkeitssinn“ nimmt die Welt, wie sie ist, nicht wahr als „die beste aller möglichen“ sondern als „Aufgabe und Erfindung“. Nur wer bereit ist die Möglichkeiten, die in der Wirklichkeit verborgen sind, zu entdecken, kann den ersten Schritt zu ihrer Verwirklichung tun.

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Laplace formulierte die kühne Behauptung, dass es möglich sei, jeden beliebigen Zustand der Welt in der Zukunft zu be- rechnen, wenn zwei Bedingungen erfüllt sind: Erstens muss man alle Anfangsbedingungen der Welt kennen, und zweitens alle mathematischen Prinzipien, die Naturgesetze.

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Kybernetik ist die Wissenschaft von der Steuerung und Regelung von Maschinen.

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ührungskräfte sind keine Kybernetiker, keine Steuermänner und -frauen, die Komplexität beherrschen, sondern Menschen, die führen können, weil sie in komple- xen Situationen und Umgebungen angemessen und wirksam entscheiden und handeln können.

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Zuversicht im Angesicht der wesentlichen Unvorhersehbar- keit und Unbeherrschbarkeit des „Systems“ Welt macht eigentliche Führungsqualität erst aus.

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By leveraging stochastic processes such as the beta and Dirichlet process (DP), these methods allow the data to drive the complexity of the learned model, while still permitting efficient inference algorithms.

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Flashcard 1729396870412

Question
As discussed later, probabilistic programming offers an elegant way of generalizing [...], allowing a much richer representation of models.
Answer
graphical models


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As discussed later, probabilistic programming offers an elegant way of generalizing graphical models, allowing a much richer representation of models.

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Flashcard 1729398443276

Question
the beta and Dirichlet process allow [...] to drive the complexity of the learned model, while still permitting efficient inference algorithms.
Answer
the data


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By leveraging stochastic processes such as the beta and Dirichlet process (DP), these methods allow the data to drive the complexity of the learned model, while still permit- ting efficient inference algorithms.

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Orien- tierungslosigkeit und Unsicherheit entstehen dann, wenn man nicht weiß, wo man sich be- findet. Wer seinen Standort, seine Position nicht kennt, weiß nicht, wohin er gehen kann – und erst recht nicht, wohin er andere führen soll.

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Flashcard 1729401589004

Question
By leveraging stochastic processes such as the beta and Dirichlet process (DP), these methods allow the data to drive the complexity of the learned model, while still permit- ting [...]
Answer
efficient inference algorithms.

Speculation: efficient is possible because DP or BP have imposed certain structure on them?


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ad><head> By leveraging stochastic processes such as the beta and Dirichlet process (DP), these methods allow the data to drive the complexity of the learned model, while still permit- ting efficient inference algorithms. <html>

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Flashcard 1729406831884

Question
the probabilistic framework to machine learning infers [...] to explain observed data
Answer
plausible models


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The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data

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Almost all machine-learning tasks can be formulated as making inferences about missing or latent data from the observed data

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Flashcard 1729419676940

Question
Almost all machine-learning tasks can be formulated as making inferences about [...] from the observed data
Answer
missing or latent data

Data can be understood in the broadest sense. It can be missing data, or model parameters, or even models themselves.


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Almost all machine-learning tasks can be formulated as making inferences about missing or latent data from the observed data

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Flashcard 1729421249804

Question
A model is considered to be well defined if it can [...] about unobserved data (BEFORE) having been trained on observed data
Answer
make forecasts or predictions


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if the model cannot make predictions it cannot be falsified

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Flashcard 1729425444108

Question
Bayesian optimization poses the question of finding function optima as a problem in [...]
Answer
sequential decision theory

For this reason it has great potential in reinforcement learning.


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#pyro
The basic unit of Pyro programs is the stochastic function.

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Models in Pyro: From Primitive Distributions to Stochastic Functions — Pyro Tutorials documentation
In [1]: # import some dependencies import torch from torch.autograd import Variable import pyro import pyro.distributions as dist Models in Pyro: From Primitive Distributions to Stochastic Functions¶ <span>The basic unit of Pyro programs is the stochastic function. This is an arbitrary Python callable that combines two ingredients: deterministic Python code; and primitive stochastic functions Concretely, a stochastic function can be any Python




#pyro
Expressing models as stochastic functions in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables.

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Models in Pyro: From Primitive Distributions to Stochastic Functions — Pyro Tutorials documentation
Torch nn.Module . Throughout the tutorials and documentation, we will often call stochastic functions models, since stochastic functions can be used to represent simplified or abstract descriptions of a process by which data are generated. <span>Expressing models as stochastic functions in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ Primitive stochastic functions, or distrib




#pyro
Primitive stochastic functions, or distributions, are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs.

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Models in Pyro: From Primitive Distributions to Stochastic Functions — Pyro Tutorials documentation
s that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ <span>Primitive stochastic functions, or distributions, are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like




#pyro
users can implement custom distributions by subclassing pyro.distributions.Distribution .

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Models in Pyro: From Primitive Distributions to Stochastic Functions — Pyro Tutorials documentation
the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like the bernoulli and uniform distributions, but <span>users can implement custom distributions by subclassing pyro.distributions.Distribution . Using primitive stochastic functions is easy. For example, to draw a sample x from the unit normal distribution (0,1)N(0,1) we do the following: In [2]: mu = Variable(torc




Probabilistic principal components analysis (PCA) is a dimensionality reduction technique that analyzes data via a lower dimensional latent space (Tipping & Bishop, 1999) .

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Edward – Probabilistic PCA
ahora Edward [imagelink] Getting Started Tutorials API Community Contributing Github [imagelink] Probabilistic PCA <span>Probabilistic principal components analysis (PCA) is a dimensionality reduction technique that analyzes data via a lower dimensional latent space (Tipping & Bishop, 1999). It is often used when there are missing values in the data or for multidimensional scaling. We demonstrate with an example in Edward. An interactive version with Jupyter notebook is a




Flashcard 1729440386316

Question
Probabilistic principal components analysis (PCA) analyzes data via a [...] (Tipping & Bishop, 1999) .
Answer
lower dimensional latent space


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Probabilistic principal components analysis (PCA) is a dimensionality reduction technique that analyzes data via a lower dimensional latent space (Tipping & Bishop, 1999) .

Original toplevel document

Edward – Probabilistic PCA
ahora Edward [imagelink] Getting Started Tutorials API Community Contributing Github [imagelink] Probabilistic PCA <span>Probabilistic principal components analysis (PCA) is a dimensionality reduction technique that analyzes data via a lower dimensional latent space (Tipping & Bishop, 1999). It is often used when there are missing values in the data or for multidimensional scaling. We demonstrate with an example in Edward. An interactive version with Jupyter notebook is a







Flashcard 1729441959180

Tags
#pyro
Question
users can implement custom distributions by [...] pyro.distributions.Distribution .
Answer
subclassing


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users can implement custom distributions by subclassing pyro.distributions.Distribution .

Original toplevel document

Models in Pyro: From Primitive Distributions to Stochastic Functions — Pyro Tutorials documentation
the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like the bernoulli and uniform distributions, but <span>users can implement custom distributions by subclassing pyro.distributions.Distribution . Using primitive stochastic functions is easy. For example, to draw a sample x from the unit normal distribution (0,1)N(0,1) we do the following: In [2]: mu = Variable(torc







Review of the uses of "se"

Definition and rules:

  1. Indirect object pronoun: Yo doy un regalo a mi amiga --> Yo le doy un regalo--> Yo se lo doy (I give a gift to my friend --> I giver her a gift --> I give it to her).
  2. Reflexive: Sentarse (to sit) --> Ellos se sentaron en primera fila (They sat on the first row).
  3. Reciprocal: Amarse (to love each other) --> Esa pareja se ama con locura(That couple love each other madly).
  4. Impersonal: Nunca se debe llegar tarde a una entrevista de trabajo (One should never arrive late to a job interview).
  5. Passive: Desde mi ventana se ve la playa (I can see the beach from my window; literal: "The beach is seen from my window).
  6. Pure pronominal: Pedro siempre se baña después del trabajo [verb bañarse] (Pedro always has a bath after work).

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Flashcard 1729446415628

Question
Review of the uses of "se"

Definition and rules:

  1. Indirect object pronoun: Yo doy un regalo a mi amiga --> Yo le doy un regalo--> Yo [...] doy (I give a gift to my friend --> I giver her a gift --> I give it to her).
Answer
se lo


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Open it
Review of the uses of "se" Definition and rules: Indirect object pronoun: Yo doy un regalo a mi amiga --> Yo le doy un regalo--> Yo se lo doy (I give a gift to my friend --> I giver her a gift --> I give it to her). Reflexive: Sentarse (to sit) --> Ellos se sentaron en primera fila (They sat on the first row). Re







Impersonal: Nunca se debe llegar tarde a una entrevista de trabajo (One should never arrive late to a job interview).

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Open it
gift --> I give it to her). Reflexive: Sentarse (to sit) --> Ellos se sentaron en primera fila (They sat on the first row). Reciprocal: Amarse (to love each other) --> Esa pareja se ama con locura(That couple love each other madly). <span>Impersonal: Nunca se debe llegar tarde a una entrevista de trabajo (One should never arrive late to a job interview). Passive: Desde mi ventana se ve la playa (I can see the beach from my window; literal: "The beach is seen from my window). Pure pronominal: Pedro siempre se baña después del trabaj




Flashcard 1729450085644

Tags
#Spanish
Question
Impersonal: [...] llegar tarde a una entrevista de trabajo (One should never arrive late to a job interview).
Answer
Nunca se debe


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Impersonal: Nunca se debe llegar tarde a una entrevista de trabajo (One should never arrive late to a job interview).

Original toplevel document

Open it
gift --> I give it to her). Reflexive: Sentarse (to sit) --> Ellos se sentaron en primera fila (They sat on the first row). Reciprocal: Amarse (to love each other) --> Esa pareja se ama con locura(That couple love each other madly). <span>Impersonal: Nunca se debe llegar tarde a una entrevista de trabajo (One should never arrive late to a job interview). Passive: Desde mi ventana se ve la playa (I can see the beach from my window; literal: "The beach is seen from my window). Pure pronominal: Pedro siempre se baña después del trabaj







Flashcard 1729452707084

Question
if the model cannot make predictions it cannot [...]
Answer
be falsified


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if the model cannot make predictions it cannot be falsified

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Flashcard 1729454279948

Tags
#pyro
Question
Expressing models as [...] in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables.
Answer
stochastic functions


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Expressing models as stochastic functions in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables.

Original toplevel document

Models in Pyro: From Primitive Distributions to Stochastic Functions — Pyro Tutorials documentation
Torch nn.Module . Throughout the tutorials and documentation, we will often call stochastic functions models, since stochastic functions can be used to represent simplified or abstract descriptions of a process by which data are generated. <span>Expressing models as stochastic functions in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ Primitive stochastic functions, or distrib







Flashcard 1729455852812

Tags
#pyro
Question
[...] are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs.
Answer
Primitive stochastic functions


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Primitive stochastic functions, or distributions, are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs.

Original toplevel document

Models in Pyro: From Primitive Distributions to Stochastic Functions — Pyro Tutorials documentation
s that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ <span>Primitive stochastic functions, or distributions, are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like







He was the son of Thomas Godwin, Bishop of Bath and Wells, born at Hannington, Northamptonshire. He was the great uncle of the writer Jonathan Swift. He was elected student of Christ Church, Oxford, in 1578, took his bachelor's degree in 1580, and that of master in 1583.

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Francis Godwin - Wikipedia
gelink] Bishop Godwin Francis Godwin (1562–1633) was an English historian, science fiction author, divine, Bishop of Llandaff and of Hereford. Contents [hide] 1 Life 2 Works 3 References 4 External links Life[edit] <span>He was the son of Thomas Godwin, Bishop of Bath and Wells, born at Hannington, Northamptonshire. He was the great uncle of the writer Jonathan Swift. He was elected student of Christ Church, Oxford, in 1578, took his bachelor's degree in 1580, and that of master in 1583. After holding two Somerset livings he was in 1587 appointed subdean of Exeter. In 1590 he accompanied William Camden on an antiquarian tour through Wales. He was created bachelor of d