50 citations · 91 across the 5 of their papers we have counts for
5 papers
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
Chengkun Li, Grégoire Clarté, Martin Jørgensen +1
In applied Bayesian inference scenarios, users may have access to a large number of pre-existing model evaluations, for example from maximum-a-posteriori (MAP) optimization runs. H…
Dynamic allocation of limited memory resources in reinforcement learning
Nisheet Patel, Luigi Acerbi, Alexandre Pouget
Biological brains are inherently limited in their capacity to process and store information, but are nevertheless capable of solving complex tasks with apparent ease. Intelligent b…
Variational Bayesian Monte Carlo with Noisy Likelihoods
Luigi Acerbi
Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box,…
Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling
Bas van Opheusden, Luigi Acerbi, Wei Ji Ma
The fate of scientific hypotheses often relies on the ability of a computational model to explain the data, quantified in modern statistical approaches by the likelihood function.…
Variational Bayesian Monte Carlo
Luigi Acerbi
Many probabilistic models of interest in scientific computing and machine learning have expensive, black-box likelihoods that prevent the application of standard techniques for Bay…