广义线性模型的并行化贝叶斯模型平均

IF 5.4 2区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Statistical Software Pub Date : 2022-01-01 DOI:10.18637/jss.v104.i02

R. Lucchetti, Luca Pedini

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引用次数: 0

摘要

本文介绍了在广义线性模型中提供贝叶斯模型平均(BMA)的gretl函数包ParMA。为了克服所涵盖的许多模型缺乏分析规范，该软件包的特点是实现了可逆跳跃马尔可夫链蒙特卡罗技术，遵循了Green(1995)的原始想法，作为对几种规范建模的灵活工具。特别注意的是计算方面，如模型建立过程的自动化和抽样方案的并行化。

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ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models

This paper describes the gretl function package ParMA , which provides Bayesian model averaging (BMA) in generalized linear models. In order to overcome the lack of analytical specification for many of the models covered, the package features an implementation of the reversible jump Markov chain Monte Carlo technique, following the original idea by Green (1995), as a flexible tool to model several specifications. Particular attention is devoted to computational aspects such as the automatization of the model building procedure and the parallelization of the sampling scheme.

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来源期刊

Journal of Statistical Software 工程技术-计算机：跨学科应用

CiteScore

10.70

自引率

1.70%

发文量

审稿时长

6-12 weeks

期刊介绍： The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.