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关 键 词：购买stata软件以及百科
行 业：IT 软件 教学管理软件
发布时间：2024-04-07
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In Bayesian analysis, we seek a balance between prior information in a form of expert knowledge or belief and evidence from data at hand. Achieving the right balance is one of the difficulties in Bayesian modeling and inference. In general, we should not allow the prior information to overwhelm the evidence from the data, especially when we have a large data sample. A famous theoretical result, the Bernstein–von Mises theorem, states that in large data samples, the posterior distribution is independent of the prior distribution and, therefore, Bayesian and likelihood-based inferences should yield essentially the same results. On the other hand, we need a strong enough prior to support weak evidence that usually comes from insufficient data. It is always good practice to perform sensitivity analysis to check the dependence of the results on the choice of a prior.
Estimate both. Visualize both.
How can the updated table command and new etable command do all that? They are built on top of a whole new system for collecting stored results from commands and displaying those results as tables. Stata 17 gives you access to that system too.
PyStata—Python and Stata Highlights Use Stata from within Python Stata API functions to run Stata commands and access Stata data and returned results from Python IPython magic command to use Stata from Jupyter Notebook
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