{"acknowledgement":"B. Sturmfels is supported in part by NSF grants DMS-0456960 and DMS-0757236. C. Uhler is supported by an International Fulbright Science and Technology Fellowship.","date_created":"2018-12-11T12:02:35Z","month":"08","citation":{"ama":"Sturmfels B, Uhler C. Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry. <i>Annals of the Institute of Statistical Mathematics</i>. 2010;62(4):603-638. doi:<a href=\"https://doi.org/10.1007/s10463-010-0295-4\">10.1007/s10463-010-0295-4</a>","mla":"Sturmfels, Bernd, and Caroline Uhler. “Multivariate Gaussians, Semidefinite Matrix Completion, and Convex Algebraic Geometry.” <i>Annals of the Institute of Statistical Mathematics</i>, vol. 62, no. 4, Springer, 2010, pp. 603–38, doi:<a href=\"https://doi.org/10.1007/s10463-010-0295-4\">10.1007/s10463-010-0295-4</a>.","short":"B. Sturmfels, C. Uhler, Annals of the Institute of Statistical Mathematics 62 (2010) 603–638.","ieee":"B. Sturmfels and C. Uhler, “Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry,” <i>Annals of the Institute of Statistical Mathematics</i>, vol. 62, no. 4. Springer, pp. 603–638, 2010.","ista":"Sturmfels B, Uhler C. 2010. Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry. Annals of the Institute of Statistical Mathematics. 62(4), 603–638.","chicago":"Sturmfels, Bernd, and Caroline Uhler. “Multivariate Gaussians, Semidefinite Matrix Completion, and Convex Algebraic Geometry.” <i>Annals of the Institute of Statistical Mathematics</i>. Springer, 2010. <a href=\"https://doi.org/10.1007/s10463-010-0295-4\">https://doi.org/10.1007/s10463-010-0295-4</a>.","apa":"Sturmfels, B., &#38; Uhler, C. (2010). Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry. <i>Annals of the Institute of Statistical Mathematics</i>. Springer. <a href=\"https://doi.org/10.1007/s10463-010-0295-4\">https://doi.org/10.1007/s10463-010-0295-4</a>"},"day":"01","page":"603 - 638","year":"2010","status":"public","publisher":"Springer","oa":1,"date_published":"2010-08-01T00:00:00Z","type":"journal_article","extern":1,"volume":62,"publist_id":"3332","date_updated":"2021-01-12T07:42:33Z","intvolume":"        62","title":"Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry","quality_controlled":0,"doi":"10.1007/s10463-010-0295-4","author":[{"full_name":"Sturmfels, Bernd","first_name":"Bernd","last_name":"Sturmfels"},{"orcid":"0000-0002-7008-0216","full_name":"Caroline Uhler","first_name":"Caroline","id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","last_name":"Uhler"}],"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/0906.3529"}],"_id":"3308","abstract":[{"text":"We study multivariate normal models that are described by linear constraints on the inverse of the covariance matrix. Maximum likelihood estimation for such models leads to the problem of maximizing the determinant function over a spectrahedron, and to the problem of characterizing the image of the positive definite cone under an arbitrary linear projection. These problems at the interface of statistics and optimization are here examined from the perspective of convex algebraic geometry.","lang":"eng"}],"publication":"Annals of the Institute of Statistical Mathematics","publication_status":"published","issue":"4"}