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tributária não a defere a pessoa jurídica de direito público diversa daquela a que a Constituição a tenha atribuído. CAPÍTULO II Limitações da Competência Tributária SEÇÃO I Disposições Gerais <span>Art. 9º É vedado à União, aos Estados, ao Distrito Federal e aos Municípios: I - instituir ou majorar tributos sem que a lei o estabeleça, ressalvado, quanto à majoração, o disposto nos artigos 21, 26 e 65; II - cobrar imposto sobre o patrimônio e a renda com base em lei posterior à data inicial do exercício financeiro a que corresponda; III - estabelecer limitações ao tráfego, no território nacional, de pessoas ou mercadorias, por meio de tributos interestaduais ou intermunicipais; IV - cobrar imposto sobre: a) o patrimônio, a renda ou os serviços uns dos outros; b) templos de qualquer culto; c) o patrimônio, a renda ou serviços de partidos políticos e de instituições de educação ou de assistência social, observados os requisitos fixados na Seção II dêste Capítulo; c) o patrimônio, a renda ou serviços dos partidos políticos, inclusive suas fundações, das entidades sindicais dos trabalhadores, das instituições de educação e de assistência social, sem fins lucrativos, observados os requisitos fixados na Seção II deste Capítulo; (Redação dada pela Lei Complementar nº 104, de 2001) d) papel destinado exclusivamente à impressão de jornais, periódicos e livros. § 1º O disposto no inciso IV não exclui a atribuição, por lei, às entidades nele referidas, da condição de responsáveis pelos tributos que lhes caiba reter na fonte, e não as dispensa d

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públicos, vamos admitir que estamos falando da responsabilidade do Estado (ou da Administração Pública, como preferir), que via de regra é objetiva, como consagra o §6º do art. 37 da CF-88: "§ <span>6º As pessoas jurídicas de direito público e as de direito privado prestadoras de serviços públicos responderão pelos danos que seus agentes, nessa qualidade, causarem a terceiros, assegurado o direito de regresso contra o responsável nos casos de dolo ou culpa." b) CERTO. Exato. Isso é um conceito que muita gente confunde e acaba errando questões como essa. O serviço público sempre será competência o Estado. O que pode acontecer é a delegação

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Among the statistical approaches, methods included time series models (N = 9 publications), generalized linear models (N = 4), Bayesian networks (N = 2), classification methods (N = 2), survival analysis (N = 1), and a prediction market (N = 1) (Table 1). The mechanistic approaches included compartmental models, which model transitions across various sub-populations (susceptible-infectious-removed [SIR] models and variants) (N = 12 publi

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models (N = 9 publications), generalized linear models (N = 4), Bayesian networks (N = 2), classification methods (N = 2), survival analysis (N = 1), and a prediction market (N = 1) (Table 1). <span>The mechanistic approaches included compartmental models, which model transitions across various sub-populations (susceptible-infectious-removed [SIR] models and variants) (N = 12 publications); and agent-based models (ABMs), which model exposure, infection, transmission and behaviors for each individual in the population (N = 5) (Table 1). <span>

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Any function of a random variable X is termed a statistic.

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Any function of a random variable X is termed a statistic.

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Calibration refers to the statistical consistency between the probabilistic forecasts and the observations, and is a joint property of the predictive distributions and the observations.

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The branching process is the linear approximation of the SIR stochastic process near the disease-free equilibrium.

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The branching process is the linear approximation of the SIR stochastic process near the disease-free equilibrium.

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spike and slab priors consist of two parts: the spike part and the slab part. The spike part governs the probability of a given variable being chosen for the model (i.e., having a non-zero coefficient). The slab part shrinks the non-zero coefficients toward pri

e, by using priors on the regressor coefficients, the model incorporates uncertainties of the coefficient estimates when producing the credible interval for the forecasts. As the name suggests, <span>spike and slab priors consist of two parts: the spike part and the slab part. The spike part governs the probability of a given variable being chosen for the model (i.e., having a non-zero coefficient). The slab part shrinks the non-zero coefficients toward prior expectations (often zero). To see how this works, let denote a vector of 1s and 0s where a value of 1 indicates that the variable is selected (non-zero coefficient). We can factorize the spike and slab prior as

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spike and slab priors consist of two parts: the spike part and the slab part. The spike part governs the probability of a given variable being chosen for the model (i.e., having a non-zero coefficient). The slab part shrinks the non-zero coefficients toward prior expectations (often zero)

e, by using priors on the regressor coefficients, the model incorporates uncertainties of the coefficient estimates when producing the credible interval for the forecasts. As the name suggests, <span>spike and slab priors consist of two parts: the spike part and the slab part. The spike part governs the probability of a given variable being chosen for the model (i.e., having a non-zero coefficient). The slab part shrinks the non-zero coefficients toward prior expectations (often zero). To see how this works, let denote a vector of 1s and 0s where a value of 1 indicates that the variable is selected (non-zero coefficient). We can factorize the spike and slab prior as

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slab priors consist of two parts: the spike part and the slab part. The spike part governs the probability of a given variable being chosen for the model (i.e., having a non-zero coefficient). <span>The slab part shrinks the non-zero coefficients toward prior expectations (often zero) <span>

e, by using priors on the regressor coefficients, the model incorporates uncertainties of the coefficient estimates when producing the credible interval for the forecasts. As the name suggests, <span>spike and slab priors consist of two parts: the spike part and the slab part. The spike part governs the probability of a given variable being chosen for the model (i.e., having a non-zero coefficient). The slab part shrinks the non-zero coefficients toward prior expectations (often zero). To see how this works, let denote a vector of 1s and 0s where a value of 1 indicates that the variable is selected (non-zero coefficient). We can factorize the spike and slab prior as

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In any emerging epidemic, underreporting is a critical challenge for ongoing assessment of this epidemic and has had enormous impact on predictions of outbreak size, but also of outbreak impact—for example, in terms of the case-fatality ratio (the proportion of cases that lead to death). Early in any outbreak, this estimate of severity can suffer from imprecise information on both the numerator (if not all deaths due to the infection are identified as such; for example,

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epidemic and has had enormous impact on predictions of outbreak size, but also of outbreak impact—for example, in terms of the case-fatality ratio (the proportion of cases that lead to death). <span>Early in any outbreak, this estimate of severity can suffer from imprecise information on both the numerator (if not all deaths due to the infection are identified as such; for example, because health services are overwhelmed caring for the sick) and the denominator (if cases are not reported or, conversely, noncases get reported as cases if they are not laboratory-confirmed). This caused problems early in the H1N1 influenza outbreak first reported in Mexico in 2009, as well as in the current Ebola outbreak. Although level of underreporting can be estimated f

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et reported as cases if they are not laboratory-confirmed). This caused problems early in the H1N1 influenza outbreak first reported in Mexico in 2009, as well as in the current Ebola outbreak. <span>Although level of underreporting can be estimated from retrospective serological studies, it is usually not identifiable in real-time data. These limitations make it almost impossible to make reliable long-term predictions. Thus, modeling results are often based on scenarios in which a pathogen spreads unaltered by behavioral changes or the public health response. This rarely reflects reality, especially i

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be estimated from retrospective serological studies, it is usually not identifiable in real-time data. These limitations make it almost impossible to make reliable long-term predictions. Thus, <span>modeling results are often based on scenarios in which a pathogen spreads unaltered by behavioral changes or the public health response. This rarely reflects reality, especially in such a devastating outbreak as Ebola, where the situation constantly changes owing to growing awareness in the community, as well as national and international intervention. Careful communication of findings is key, and data and methods of analysis (including code) must be made freely available to the wider research community. Only in this way can reproduci