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by Bernd Sturmfels,Seth Sullivant,Mathias Drton
Download Lectures on Algebraic Statistics (Oberwolfach Seminars) fb2
Mathematics
  • Author:
    Bernd Sturmfels,Seth Sullivant,Mathias Drton
  • ISBN:
    3764389044
  • ISBN13:
    978-3764389048
  • Genre:
  • Publisher:
    Birkhäuser; 2009 edition (December 18, 2008)
  • Pages:
    172 pages
  • Subcategory:
    Mathematics
  • Language:
  • FB2 format
    1900 kb
  • ePUB format
    1864 kb
  • DJVU format
    1291 kb
  • Rating:
    4.6
  • Votes:
    196
  • Formats:
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The seminar lectures provided an introduction to some of the fundamental notions in algebraic statistics, as well as a. .

For a, b ∈ N, let {A ⊆ : b}. b. Question.

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 o.

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

How does an algebraic geometer studying secant varieties further the understanding of hypothesis tests in statistics? . The goal of these lectures is to introduce newcomers from the different camps to 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

Lectures on Algebraic Statistics. Authors: Drton, Mathias, Sturmfels, Bernd, Sullivant, Seth. The book certainly achieves the authors’ stated aims of encouraging ‘dialogue between algebra and statistics, to benefit both disciplines.

Lectures on Algebraic Statistics. Introduces the relatively new field of Algebraic Statistics.

Электронная книга "Lectures on Algebraic Statistics", Mathias Drton, Bernd Sturmfels, Seth Sullivant. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Lectures on Algebraic Statistics" для чтения в офлайн-режиме.

Lectures on Algebraic Statistics book. Mathias Drton, Bernd Sturmfels. How does an algebraic geometer studying secant varieties. 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?

University of California, Berkeley. Algebraic methods have a long history in statistics.

University of California, Berkeley. North Carolina State University. The most prominent manifestation of modern algebra in statistics can be seen in the field of algebraic statistics, which brings tools from commutative algebra and algebraic geometry to bear on statistical problems. Now over two decades old, algebraic statistics has applications in a wide range of theoretical and applied statistical domains.

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 behaviour of widely used statistical inference procedures; computational algebraic geometry can be used to study parameter spaces and other features of statistical models.