Mathematisches Forschungsinstitut Oberwolfach Algebraic Statistics
Mathematisches Forschungsinstitut Oberwolfach Report No. 20/2017 DOI: 10.4171/OWR/2017/20 Algebraic Statistics Organised by Mathias Drton, Seattle Thomas Kahle, Magdeburg Bernd Sturmfels, Berkeley Caroline Uhler, Cambridge MA 16 April – 22 April 2017 Abstract. Algebraic Statistics is concerned with the interplay of techniques from commutative algebra, combinatorics, (real) algebraic geometry, and re- lated fields with problems arising in statistics and data science. This work- shop was the first at Oberwolfach dedicated to this emerging subject area. The participants highlighted recent achievements in this field, explored ex- citing new applications, and mapped out future directions for research. Mathematics Subject Classification (2010): Primary: 13P25, 62E99; Secondary: 15A72, 52A40, 92B05. Introduction by the Organisers The Oberwolfach workshop Algebraic statistics was organized by Mathias Drton, Thomas Kahle, Bernd Sturmfels, and Caroline Uhler and ran April 17-21, 2017. Algebraic statistics is a rather new field, about two decades old. The field emerged from two lines of work: Diaconis and Sturmfels introduced algebraic tools to cat- egorical data analysis and suggested the construction of Markov bases to perform exact goodness-of-fit tests for such data. This got algebraists, combinatorialists, and algebraic geometers interested in problems in statistics. Significant contribu- tions from Japanese statisticians resulted in a book on Markov bases in algebraic statistics. Through recent work Markov bases have also found applications to disclosure limitation and genetics. The second source, which coined the term ‘al- gebraic statistics’, is a book explaining how Gr¨obner basis methods can be used in experimental design. A recent direction that emerged from this is the use of 1208 Oberwolfach Report 20/2017 commutative algebra for experimental design in system reliability.
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