{"date_published":"2018-01-01T00:00:00Z","issue":"1","publist_id":"6284","citation":{"chicago":"Mohammadi, Fatemeh, Caroline Uhler, Charles Wang, and Josephine Yu. “Generalized Permutohedra from Probabilistic Graphical Models.” SIAM Journal on Discrete Mathematics. SIAM, 2018. https://doi.org/10.1137/16M107894X.","short":"F. Mohammadi, C. Uhler, C. Wang, J. Yu, SIAM Journal on Discrete Mathematics 32 (2018) 64–93.","ista":"Mohammadi F, Uhler C, Wang C, Yu J. 2018. Generalized permutohedra from probabilistic graphical models. SIAM Journal on Discrete Mathematics. 32(1), 64–93.","ama":"Mohammadi F, Uhler C, Wang C, Yu J. Generalized permutohedra from probabilistic graphical models. SIAM Journal on Discrete Mathematics. 2018;32(1):64-93. doi:10.1137/16M107894X","apa":"Mohammadi, F., Uhler, C., Wang, C., & Yu, J. (2018). Generalized permutohedra from probabilistic graphical models. SIAM Journal on Discrete Mathematics. SIAM. https://doi.org/10.1137/16M107894X","ieee":"F. Mohammadi, C. Uhler, C. Wang, and J. Yu, “Generalized permutohedra from probabilistic graphical models,” SIAM Journal on Discrete Mathematics, vol. 32, no. 1. SIAM, pp. 64–93, 2018.","mla":"Mohammadi, Fatemeh, et al. “Generalized Permutohedra from Probabilistic Graphical Models.” SIAM Journal on Discrete Mathematics, vol. 32, no. 1, SIAM, 2018, pp. 64–93, doi:10.1137/16M107894X."},"title":"Generalized permutohedra from probabilistic graphical models","language":[{"iso":"eng"}],"quality_controlled":"1","day":"01","oa":1,"intvolume":" 32","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T11:50:06Z","author":[{"first_name":"Fatemeh","full_name":"Mohammadi, Fatemeh","id":"2C29581E-F248-11E8-B48F-1D18A9856A87","last_name":"Mohammadi"},{"id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","full_name":"Uhler, Caroline","last_name":"Uhler","orcid":"0000-0002-7008-0216","first_name":"Caroline"},{"first_name":"Charles","last_name":"Wang","full_name":"Wang, Charles"},{"first_name":"Josephine","last_name":"Yu","full_name":"Yu, Josephine"}],"month":"01","doi":"10.1137/16M107894X","type":"journal_article","abstract":[{"lang":"eng","text":"A graphical model encodes conditional independence relations via the Markov properties. For an undirected graph these conditional independence relations can be represented by a simple polytope known as the graph associahedron, which can be constructed as a Minkowski sum of standard simplices. We show that there is an analogous polytope for conditional independence relations coming from a regular Gaussian model, and it can be defined using multiinformation or relative entropy. For directed acyclic graphical models we give a construction of this polytope as a Minkowski sum of matroid polytopes. Finally, we apply this geometric insight to construct a new ordering-based search algorithm for causal inference via directed acyclic graphical models. "}],"page":"64-93","publisher":"SIAM","extern":"1","main_file_link":[{"url":"https://arxiv.org/abs/1606.01814","open_access":"1"}],"publication_status":"published","date_updated":"2021-01-12T06:48:13Z","year":"2018","_id":"1092","status":"public","oa_version":"Preprint","volume":32,"publication":"SIAM Journal on Discrete Mathematics"}