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关 键 词:Stata管理工具
行 业:IT 软件 教学管理软件
发布时间:2024-03-24
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Bayesian inference provides a straightforward and more intuitive interpretation of the results in terms of probabilities. For example, credible intervals are interpreted as intervals to which parameters belong with a certain probability, unlike the less straightforward repeated-sampling interpretation of the confidence intervals. Bayesian models satisfy the likelihood principle (Berger and Wolpert 1988) that the information in a sample is fully represented by the likelihood function. This principle requires that if the likelihood function of one model is proportional to the likelihood function of another model, then inferences from the two models should give the same results. Some researchers argue that frequentist methods that depend on the experimental design may violate the likelihood principle.
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Causal inference, average treatment effects, potential-outcome means, double-robust estimation
Linear, generalized linear, and nonlinear models Random effects and latent factors Flexible control of MCMC sampling Multiple chains Postestimation Convergence diagnostics Posterior means, medians, and standard deviations
More multilevel models.
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