495 citations · 932 across the 37 of their papers we have counts for
5 papers · 1 filter
Adaptive Approximate Bayesian Computation Tolerance Selection
Umberto Simola, Jessica Cisewski-Kehe, Michael U. Gutmann +1
Approximate Bayesian Computation (ABC) methods are increasingly used for inference in situations in which the likelihood function is either computationally costly or intractable to…
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
Marko Järvenpää, Michael Gutmann, Aki Vehtari +1
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical mode…
To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions
Tatiana Lopez-Guevara, Rita Pucci, Nicholas Taylor +3
Our brains are able to exploit coarse physical models of fluids to solve everyday manipulation tasks. There has been considerable interest in developing such a capability in robots…
Robust Optimisation Monte Carlo
Borislav Ikonomov, Michael U. Gutmann
This paper is on Bayesian inference for parametric statistical models that are defined by a stochastic simulator which specifies how data is generated. Exact sampling is then possi…
Adaptive Gaussian Copula ABC
Yanzhi Chen, Michael U. Gutmann
Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents…