35 citations · 48 across the 4 of their papers we have counts for
5 papers
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
Marko Järvenpää, Aki Vehtari, Pekka Marttinen
The computational efficiency of approximate Bayesian computation (ABC) has been improved by using surrogate models such as Gaussian processes (GP). In one such promising framework…
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…
A Bayesian model of acquisition and clearance of bacterial colonization
Marko Järvenpää, Mohamad R. Abdul Sater, Georgia K. Lagoudas +6
Bacterial populations that colonize a host play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerg…
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…