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关 键 词：stata软件免费版
行 业：IT 软件 教学管理软件
发布时间：2024-03-31
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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;
比较容易想到的是: . 贝叶斯多级模型 . 门限回归 . 具有随机系数的面板数据tobit . 区间测量结果的多层回归 . 删失结果的多级Tobit回归 . 面板数据的协整测试 . 时间序列中多断点的测试 . 多组广义 SEM . 异方差的线性回归 . Heckman风格的样本选择Poisson模型 . 具有随机系数的面板数据非线性模型 . 贝叶斯面板数据模型 . 随机系数的面板数据区间回归 . SVG的导出 . 贝叶斯生存模型 . 零膨胀有序概率 . 添加您自己的电源和样本大小的方法 . 贝叶斯样本选择模型 . 支持瑞典语 . 对DO文件编辑器的改进 . 流随机数生成器 . 对于java插件的改进 . Stata / MP更多的并行化
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.
Bayesian hypothesis testing can take two forms, which we refer to as interval-hypothesis testing and model-hypothesis testing. In an interval-hypothesis testing, the probability that a parameter or a set of parameters belongs to a particular interval or intervals is computed. In model hypothesis testing, the probability of a Bayesian model of interest given the observed data is computed. Model comparison is another common step of Bayesian analysis. The Bayesian framework provides a systematic and consistent approach to model comparison using the notion of posterior odds and related to them Bayes factors. See [BAYES] bayesstats ic for details. Finally, prediction of some future unobserved data may also be of interest in Bayesian analysis. The prediction of a new data point is performed conditional on the observed data using the so-called posterior predictive distribution, which involves integrating out all parameters from the model with respect to their posterior distribution. Again, Monte Carlo integration is often the only feasible option for obtaining predictions. Prediction can also be helpful in estimating the goodness of fit of a model.
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