Variational Bayesian Monte Carlo (VBMC): Bayesian inference

Variational Bayesian Monte Carlo (VBMC) algorithm for Bayesian posterior and model inference in MATLAB

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