33 citations · 44 across the 6 of their papers we have counts for
6 papers
On predictive inference for intractable models via approximate Bayesian computation
Marko Järvenpää, Jukka Corander
Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based models whose likelihood function cannot be eva…
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC
Marko Järvenpää, Jukka Corander
We present a framework for approximate Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained due to computational constraints, which is…
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…
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…
ELFI: Engine for Likelihood-Free Inference
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö +7
Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components…
Bayesian Hierarchical Model of Total Variation Regularisation for Image Deblurring
Marko Järvenpää, Robert Piché
A Bayesian hierarchical model for total variation regularisation is presented in this paper. All the parameters of an inverse problem, including the "regularisation parameter", are…