18 citations · 18 across the 7 of their papers we have counts for
7 papers
Surrogate-based ABC matches generalized Bayesian inference under specific discrepancy and kernel choices
Marko Järvenpää, Jukka Corander, Henri Pesonen
Generalized Bayesian inference (GBI) is an alternative inference framework motivated by robustness to modeling errors, where a specific loss function is used to link the model para…
Stratified distance space improves the efficiency of sequential samplers for approximate Bayesian computation
Henri Pesonen, Jukka Corander
Approximate Bayesian computation (ABC) methods are standard tools for inferring parameters of complex models when the likelihood function is analytically intractable. A popular app…
ABC of the Future
Henri Pesonen, Umberto Simola, Alvaro Köhn-Luque +8
Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are…
Prior knowledge elicitation: The past, present, and future
Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli +9
Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. I…
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Alexander Aushev, Thong Tran, Henri Pesonen +2
Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a…
Generalised Bayes Updates with -divergences through Probabilistic Classifiers
Owen Thomas, Henri Pesonen, Jukka Corander
A stream of algorithmic advances has steadily increased the popularity of the Bayesian approach as an inference paradigm, both from the theoretical and applied perspective. Even wi…
Likelihood-Free Inference with Deep Gaussian Processes
Alexander Aushev, Henri Pesonen, Markus Heinonen +2
In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations. The current state-of-the-art performance…