Lectures on algebraic statistics

By:

Sullivant, Seth

Series: Oberwolfach Seminars, 39Publication details: Basel: Birkhäuser, 2009Description: viii, 171p.: pbk.: 23cmISBN:

9783764389048

Subject(s):

DDC classification:

512.9 SUL

Summary: How does an algebraic geometer studying secant varieties further the understanding of hypothesis tests in statistics? Why would a statistician working on factor analysis raise open problems about determinantal varieties? Connections of this type are at the heart of the new field of "algebraic statistics". In this field, mathematicians and statisticians come together to solve statistical inference problems using concepts from algebraic geometry as well as related computational and combinatorial techniques. The goal of these lectures is to introduce newcomers from the different camps to algebraic statistics. The introduction will be centered around the following three observations: many important statistical models correspond to algebraic or semi-algebraic sets of parameters; the geometry of these parameter spaces determines the behavior of widely used statistical inference procedures; computational algebraic geometry can be used to study parameter spaces and other features of statistical models. https://link.springer.com/book/10.1007/978-3-7643-8905-5

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	512.9 SUL (Browse shelf(Opens below))	Available		034241

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How does an algebraic geometer studying secant varieties further the understanding of hypothesis tests in statistics? Why would a statistician working on factor analysis raise open problems about determinantal varieties? Connections of this type are at the heart of the new field of "algebraic statistics". In this field, mathematicians and statisticians come together to solve statistical inference problems using concepts from algebraic geometry as well as related computational and combinatorial techniques. The goal of these lectures is to introduce newcomers from the different camps to algebraic statistics. The introduction will be centered around the following three observations: many important statistical models correspond to algebraic or semi-algebraic sets of parameters; the geometry of these parameter spaces determines the behavior of widely used statistical inference procedures; computational algebraic geometry can be used to study parameter spaces and other features of statistical models.

https://link.springer.com/book/10.1007/978-3-7643-8905-5

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