dc.contributor.author | Bulso, Nicola | |
dc.contributor.author | Marsili, Matteo | |
dc.contributor.author | Roudi, Yasser | |
dc.date.accessioned | 2017-12-11T09:48:09Z | |
dc.date.available | 2017-12-11T09:48:09Z | |
dc.date.created | 2016-08-15T03:20:20Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Journal of Statistical Mechanics: Theory and Experiment. 2016, . | nb_NO |
dc.identifier.issn | 1742-5468 | |
dc.identifier.uri | http://hdl.handle.net/11250/2469927 | |
dc.description.abstract | We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and using a closed form formula, analytically derived by calculating the posterior probability for every possible model explaining a two body system using Jeffreys prior. The approach does not rely on the optimisation of any cost functions and consequently is much faster than existing algorithms. Despite this time and computational advantage, numerical results show that for several sparse topologies the algorithm is comparable to the best existing algorithms, and is more accurate in the presence of hidden variables. We apply this approach to the analysis of US stock market data and to neural data, in order to show its efficiency in recovering robust statistical dependencies in real data with non stationary correlations in time and space. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | IOP Publishing | nb_NO |
dc.relation.uri | http://arxiv.org/abs/1603.00952 | |
dc.title | Sparse model selection in the highly under-sampled regime | nb_NO |
dc.type | Journal article | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 49 | nb_NO |
dc.source.journal | Journal of Statistical Mechanics: Theory and Experiment | nb_NO |
dc.identifier.doi | 10.1088/1742-5468/2016/09/093404 | |
dc.identifier.cristin | 1372651 | |
dc.relation.project | Norges forskningsråd: Centre for Neural Computation, grant number 223262 | nb_NO |
dc.relation.project | Notur/NorStore: Marie Curie Training Network NETADIS(FP7, grant 290038) | nb_NO |
dc.description.localcode | This is an author-created, un-copyedited version of an article accepted for publication/published in [Journal of Statistical Mechanics: Theory and Experiment]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://iopscience.iop.org/article/10.1088/1742-5468/2016/09/093404/meta | nb_NO |
cristin.unitcode | 194,65,60,0 | |
cristin.unitname | Kavliinstitutt for nevrovitenskap | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |