
Gradientbased Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
We introduce a framework for Bayesian experimental design (BED) with imp...
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Telescoping DensityRatio Estimation
Densityratio estimation via classification is a cornerstone of unsuperv...
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Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Bayesian experimental design (BED) is a framework that uses statistical ...
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Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Implicit stochastic models, where the datageneration distribution is in...
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Adaptive Approximate Bayesian Computation Tolerance Selection
Approximate Bayesian Computation (ABC) methods are increasingly used for...
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To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions
Our brains are able to exploit coarse physical models of fluids to solve...
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Robust Optimisation Monte Carlo
This paper is on Bayesian inference for parametric statistical models th...
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Adaptive Gaussian Copula ABC
Approximate Bayesian computation (ABC) is a set of techniques for Bayesi...
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Dynamic Likelihoodfree Inference via Ratio Estimation (DIRE)
Parametric statistical models that are implicitly defined in terms of a ...
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Conditional NoiseContrastive Estimation of Unnormalised Models
Many parametric statistical models are not properly normalised and only ...
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Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models
Deep generative models can learn to generate realisticlooking images on...
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VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Deep generative models provide powerful tools for distributions over com...
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Efficient acquisition rules for modelbased approximate Bayesian computation
Approximate Bayesian computation (ABC) is a method for Bayesian inferenc...
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Simultaneous Estimation of NonGaussian Components and their Correlation Structure
The statistical dependencies which independent component analysis (ICA) ...
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Classification and Bayesian Optimization for LikelihoodFree Inference
Some statistical models are specified via a data generating process for ...
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Bayesian Optimization for LikelihoodFree Inference of SimulatorBased Statistical Models
Our paper deals with inferring simulatorbased statistical models given ...
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Likelihoodfree inference via classification
Increasingly complex generative models are being used across disciplines...
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Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
We propose a new method for detecting changes in Markov network structur...
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Michael U. Gutmann
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