Fast post-process Bayesian inference with Sparse Variational Bayesian Monte Carlo. PyVBMC: Efficient Bayesian inference in Python. Huggins B*, Li C*, Tobaben M*, Aarnos MJ, Acerbi L (2023).Advances in Neural Information Processing Systems 33 (NeurIPS '20), Montréal, Canada. Variational Bayesian Monte Carlo with Noisy Likelihoods. Machine Learning Research 96: 1-10. 1st Symposium on Advances in Approximate Bayesian Inference, Montréal, Canada. An Exploration of Acquisition and Mean Functions in Variational Bayesian Monte Carlo. Advances in Neural Information Processing Systems 31 (NeurIPS '18), Montréal, Canada. VBMC runs with virtually no tuning, comes with extensive documentation and examples, and is very easy to set up (especially if you are already familiar with our optimization toolbox, BADS).
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