9 citations · 41 across the 11 of their papers we have counts for
5 papers · 1 filter
Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
Jukka Corander, Ulpu Remes, Ida Holopainen +1
Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities. The primary fo…
Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood
Johan Pensar, Henrik Nyman, Jukka Corander
Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph. To allow for more expressi…
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…
Probabilistic elicitation of expert knowledge through assessment of computer simulations
Owen Thomas, Henri Pesonen, Jukka Corander
We present a new method for probabilistic elicitation of expert knowledge using binary responses of human experts assessing simulated data from a statistical model, where the param…
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers
Owen Thomas, Jukka Corander
Model misspecification is a long-standing enigma of the Bayesian inference framework as posteriors tend to get overly concentrated on ill-informed parameter values towards the larg…