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
 Acerbi, L. (2018). Variational Bayesian Monte Carlo. In Advances in Neural Information Processing Systems 31: 8222-8232.
 Acerbi, L. (2020). Variational Bayesian Monte Carlo with Noisy Likelihoods. In Advances in Neural Information Processing Systems 33: 8211-8222.
 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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