18797

Video URL

https://simons.berkeley.edu/talks/groups-and-symmetries-statistical-models

Groups And Symmetries In Statistical Models

(2021). Groups And Symmetries In Statistical Models. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/groups-and-symmetries-statistical-models

Groups And Symmetries In Statistical Models. The Simons Institute for the Theory of Computing, Nov. 30, 2021, https://simons.berkeley.edu/talks/groups-and-symmetries-statistical-models 

          @misc{ scivideos_18797,
            doi = {},
            url = {https://simons.berkeley.edu/talks/groups-and-symmetries-statistical-models},
            author = {},
            keywords = {},
            language = {en},
            title = {Groups And Symmetries In Statistical Models},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2021},
            month = {nov},
            note = {18797 see, \url{https://scivideos.org/index.php/Simons-Institute/18797}}
          }
Anna Seigal (Harvard University)
Talk number18797
Source RepositorySimons Institute
Subject

Abstract

Groups and symmetries are at the heart of many problems in statistics and data analysis. I will focus on parameter estimation in statistical models via maximum likelihood estimation. We will see connections between maximum likelihood estimation, linear algebra, and invariant theory. The group or symmetric structure of a statistical model can be used to capture the existence and uniqueness of a maximum likelihood estimate, as well as to suggest suitable algorithms to find it. This talk is based on joint work with Carlos Améndola, Kathlén Kohn, Visu Makam, and Philipp Reichenbach.