495 citations · 932 across the 37 of their papers we have counts for
5 papers · 1 filter
Extending the statistical software package Engine for Likelihood-Free Inference
Vasileios Gkolemis, Michael Gutmann
Bayesian inference is a principled framework for dealing with uncertainty. The practitioner can perform an initial assumption for the physical phenomenon they want to model (prior…
Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen, Dinghuai Zhang, Michael Gutmann +2
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractab…
Telescoping Density-Ratio Estimation
Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and ge…
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Steven Kleinegesse, Christopher Drovandi, Michael U. Gutmann
Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment…
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Steven Kleinegesse, Michael U. Gutmann
Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences. The models typically have free p…