135 citations · 886 across the 57 of their papers we have counts for
11 papers · 1 filter
Learning pairwise Markov network structures using correlation neighborhoods
Juri Kuronen, Jukka Corander, Johan Pensar
Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from da…
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…
Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander +2
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called like…
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
Janne Leppä-aho, Johan Pensar, Teemu Roos +1
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression…
Fast k-NN search
Ville Hyvönen, Teemu Pitkänen, Sotiris Tasoulis +5
Efficient index structures for fast approximate nearest neighbor queries are required in many applications such as recommendation systems. In high-dimensional spaces, many conventi…
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
Michael U. Gutmann, Jukka Corander
Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generat…