9 citations · 9 across the 1 of their papers we have counts for
3 papers
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…
Learning performance in inverse Ising problems with sparse teacher couplings
Alia Abbara, Yoshiyuki Kabashima, Tomoyuki Obuchi +1
We investigate the learning performance of the pseudolikelihood maximization method for inverse Ising problems. In the teacher-student scenario under the assumption that the teache…
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…