gretl 中的贝叶斯回归模型:BayTool 软件包

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY Computational Statistics Pub Date : 2024-02-21 DOI:10.1007/s00180-024-01466-5
Luca Pedini
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引用次数: 0

摘要

本文介绍的 gretl 软件包 BayTool 整合了软件功能(主要涉及频繁主义方法)和常用计量经济学模型的贝叶斯估计方法。通过广泛使用吉布斯采样进行后验模拟,并通过并行化将单线程实验分拆到多个内核或多台机器上的可能性,实现了计算效率。从用户的角度来看,该软件包只需要具备基本的 gretl 脚本知识就能完全使用其功能,同时还以图形界面的形式为经验不足的用户提供了点选式解决方案。这些特点尤其使 BayTool 成为出色的教学设备,而不会牺牲更高级或更复杂的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Bayesian regression models in gretl: the BayTool package

This article presents the gretl package BayTool which integrates the software functionalities, mostly concerned with frequentist approaches, with Bayesian estimation methods of commonly used econometric models. Computational efficiency is achieved by pairing an extensive use of Gibbs sampling for posterior simulation with the possibility of splitting single-threaded experiments into multiple cores or machines by means of parallelization. From the user’s perspective, the package requires only basic knowledge of gretl scripting to fully access its functionality, while providing a point-and-click solution in the form of a graphical interface for a less experienced audience. These features, in particular, make BayTool stand out as an excellent teaching device without sacrificing more advanced or complex applications.

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来源期刊
Computational Statistics
Computational Statistics 数学-统计学与概率论
CiteScore
2.90
自引率
0.00%
发文量
122
审稿时长
>12 weeks
期刊介绍: Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing. CompStat publishes hardware, software plus package reports.
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