35 citations · 39 across the 3 of their papers we have counts for
4 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…
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…
Efficient acquisition rules for model-based approximate Bayesian computation
Marko Järvenpää, Michael U. Gutmann, Arijus Pleska +2
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms…
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Marko Järvenpää, Michael Gutmann, Aki Vehtari +1
Approximate Bayesian computation (ABC) can be used for model fitting when the likelihood function is intractable but simulating from the model is feasible. However, even a single e…