Variational Bayesian Monte Carlo (VBMC): Bayesian inference

版本 1.0.6 (1.6 MB) 作者: Luigi Acerbi
Variational Bayesian Monte Carlo (VBMC) algorithm for Bayesian posterior and model inference in MATLAB
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更新时间 2021/6/18
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VBMC is an approximate Bayesian inference method designed to fit and evaluate computational models with a limited budget of potentially noisy likelihood evaluations (e.g., for computationally expensive models) [1,2]. Specifically, VBMC simultaneously computes:

- an approximate Bayesian posterior distribution of the model parameters;
- an approximation — technically, an approximate lower bound — of the log model evidence (also known as log marginal likelihood or log Bayes factor), a metric used for Bayesian model selection.

Extensive benchmarks on both artificial test problems and a large number of real model-fitting problems from computational and cognitive neuroscience show that VBMC generally — and often vastly — outperforms alternative methods for sample-efficient Bayesian inference.

VBMC runs with virtually no tuning and it is very easy to set up for your problem.

*** For extensive information, tutorials and documentation, please visit the GitHub page of the project: https://github.com/lacerbi/vbmc ***

If you are interested in point estimates of the parameters, you might want to check out Bayesian Adaptive Direct Search (BADS), an optimization method for model-fitting which can be used in synergy with VBMC: https://github.com/lacerbi/bads

引用格式

[3] Acerbi, L. (2019). An Exploration of Acquisition and Mean Functions in Variational Bayesian Monte Carlo. In Proc. Machine Learning Research 96: 1-10. 1st Symposium on Advances in Approximate Bayesian Inference, Montréal, Canada.

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版本 已发布 发行说明
1.0.6

See release notes for this release on GitHub: https://github.com/lacerbi/vbmc/releases/tag/v1.0.6
要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 仓库。
要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 仓库。