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关 键 词：Stata主菜单
行 业：IT 软件 教学管理软件
发布时间：2023-12-13
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the ability to use Stata from an IPython kernel-based environment like Jupyter Notebook, Spyder IDE, or PyCharm IDE; the ability to use Stata from Python , like the Windows Command Prompt, the macOS terminal, or the Unix terminal;
Posterior / Likelihood Prior If the posterior distribution can be derived in a closed form, we may proceed directly to the inference stage of Bayesian analysis. Unfortunately, except for some special models, the posterior distribution is rarely available explicitly and needs to be estimated via simulations. MCMC sampling can be used to simulate potentially very complex posterior models with an arbitrary level of precision. MCMC methods for simulating Bayesian models are often demanding in terms of specifying an efficient sampling algorithm and verifying the convergence of the algorithm to the desired posterior distribution. Inference is the next step of Bayesian analysis.
Building a reliable Bayesian model requires extensive experience from the researchers, which leads to the second difficulty in Bayesian analysis—setting up a Bayesian model and performing analysis is a demanding and involving task. This is true, however, to an extent for any statistical modeling procedure. Lastly, one of the main disadvantages of Bayesian analysis is the computational cost. As a rule, Bayesian analysis involves intractable integrals that can only be computed using intensive numerical methods. Most of these methods such as MCMC are stochastic by nature and do not comply with the natural expectation from a user of obtaining deterministic results. Using simulation methods does not compromise the discussed advantages of Bayesian approach, but unquestionably adds to the complexity of its applic
For instance, a 95% credible interval—a Bayesian counterpart of a confidence interval—can be interpreted as a range in which a parameter lies with a 0.95 probability.
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