activity
20142019
most citedInferring Cognitive Models from Data using Approximate Bayesian Computation

56 citations · 79 across the 5 of their papers we have counts for

collaborators

5 papers

stat.CO20199 cited

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…

cs.HC201756 cited

Inferring Cognitive Models from Data using Approximate Bayesian Computation

Antti Kangasrääsiö, Kumaripaba Athukorala, Andrew Howes +3

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial comp…

math.ST2014

Stratified Gaussian Graphical Models

Henrik Nyman, Johan Pensar, Jukka Corander

Gaussian graphical models represent the backbone of the statistical toolbox for analyzing continuous multivariate systems. However, due to the intrinsic properties of the multivari…

q-bio.GN201414 cited

SEK: Sparsity exploiting -mer-based estimation of bacterial community composition

Saikat Chatterjee, David Koslicki, Siyuan Dong +8

Motivation: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. Since the sample sequence data…

cs.IT2014

On the Outage Capacity of Orthogonal Space-time Block Codes Over Multi-cluster Scattering MIMO Channels

Lu Wei, Zhong Zheng, Jukka Corander +1

Multiple cluster scattering MIMO channel is a useful model for pico-cellular MIMO networks. In this paper, orthogonal space-time block coded transmission over such a channel is con…