本地化服务_提供stata解决方案和培训
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关 键 词：提供stata解决方案和培训
行 业：IT 软件 辅助设计
发布时间：2022-01-18
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We consider two types of CRIs. The first one is based on quantiles. The second one is the highest
posterior density (HPD) interval.
An f(1 �� ) 100g% quantile-based, or also known as an equal-tailed CRI, is defined as
(q =2; q1�� =2), where qa denotes the ath quantile of the posterior distribution. A commonly reported
equal-tailed CRI is (q0:025; q0:975).
HPD interval is defined as an f(1 �� ) 100g% CRI of the shortest width. As its name implies,
this interval corresponds to the region of the posterior density with the highest concentration. For a
unimodal posterior distribution, HPD is unique, but for a multimodal distribution it may not be unique.
Computational approaches for calculating HPD are described in Chen and Shao (1999) and Eberly
and Casella (2003).
In Stata 16, you can embed and execute Python code from within Stata. Stata's new python command allows you to easily call Python from Stata and output Python results within Stata.
You can invoke Python interactively or in do-files and ado-files so that you can leverage Python's extensive language features. You can also execute a Python file (.py) directly through Stata.
In addition, we introduced the Stata Function Interface (sfi) Python module, which provides a bi-directional connection between Stata and Python. This module lets you access Stata's current dataset, frames, macros, scalars, matrices, value labels, characteristics, global Mata matrices, and more.
All of this means that you can now use any Python package directly within Stata. For instance, you can use Matplotlib to draw 3-dimensional graphs. You can use NumPy for numerical computations. You can use Scrapy to scrape data from the web. You can access additional machine-learning techniques such as neural networks and support vector machines through TensorFlow and scikit-learn. And much more.
Finally, Stata’s Do-file Editor now includes syntax highlighting for the Python language.
While advanced users and programmers might be most likely to take advantage of Python integration, the availability of Python within Stata will excite many more users in all disciplines.
The posterior density (shown in red) is more peaked and shifted to the left compared with the prior
distribution (shown in blue). The posterior distribution combined the prior information about with
intro — Introduction to Bayesian analysis 3
the information from the data, from which y = 0 provided evidence for a low value of and shifted
the prior density to the left to form the posterior density. Based on this posterior distribution, the
posterior mean estimate of is 2=(2 + 40) = 0.048 and the posterior probability that, for example,
< 0.10 is about 93%.
If we compute a standard frequentist estimate of a population proportion as a fraction of the
infected subjects in the sample, y = y=n, we will obtain 0 with the corresponding 95% confidence
interval (y �� 1.96
p
y (1 �� y)=n; y + 1.96
p
y (1 �� y)=n) reducing to 0 as well. It may be difficult
to convince a health policy maker that the prevalence of the disease in that city is indeed 0, given
the small sample size and the prior information available from comparable cities about a nonzero
prevalence of this disease.
Frequentist analysis is entirely data-driven and strongly depends on whether or not the data
assumptions required by the model are met. On the other hand, Bayesian analysis provides a more
robust estimation approach by using not only the data at hand but also some existing information or
knowledge about model parameters.
In frequentist statistics, estimators are used to approximate the true values of the unknown parameters,
whereas Bayesian statistics provides an entire distribution of the parameters. In our example of a
prevalence of an infectious disease from What is Bayesian analysis?, frequentist analysis produced one
point estimate for the prevalence, whereas Bayesian analysis estimated the entire posterior distribution
of the prevalence based on a given sample.
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