9 citations · 41 across the 11 of their papers we have counts for
4 papers · 1 filter
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
Kimmo Suotsalo, Yingying Xu, Jukka Corander +1
Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically ass…
Adaptive Approximate Bayesian Computation Tolerance Selection
Umberto Simola, Jessica Cisewski-Kehe, Michael U. Gutmann +1
Approximate Bayesian Computation (ABC) methods are increasingly used for inference in situations in which the likelihood function is either computationally costly or intractable to…
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study
Johan Pensar, Yingying Xu, Santeri Puranen +3
Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades. As a result of the general appl…
Classification and Bayesian Optimization for Likelihood-Free Inference
Michael U. Gutmann, Jukka Corander, Ritabrata Dutta +1
Some statistical models are specified via a data generating process for which the likelihood function cannot be computed in closed form. Standard likelihood-based inference is then…