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Titlebook: Lectures on Algebraic Statistics; Mathias Drton,Bernd Sturmfels,Seth Sullivant Textbook 2009 Birkh?user Basel 2009 Factor analysis.Grad.Li

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發(fā)表于 2025-3-21 18:50:52 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Lectures on Algebraic Statistics
編輯Mathias Drton,Bernd Sturmfels,Seth Sullivant
視頻videohttp://file.papertrans.cn/584/583471/583471.mp4
概述Exercises and Open Problems complement the material and stimulate further research.Introduces to the rather new field of Algebraic Statistics.Includes supplementary material:
叢書名稱Oberwolfach Seminars
圖書封面Titlebook: Lectures on Algebraic Statistics;  Mathias Drton,Bernd Sturmfels,Seth Sullivant Textbook 2009 Birkh?user Basel 2009 Factor analysis.Grad.Li
描述.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..
出版日期Textbook 2009
關(guān)鍵詞Factor analysis; Grad; Likelihood; Parameter; algebraic geometry; algebraic statistics; statistics
版次1
doihttps://doi.org/10.1007/978-3-7643-8905-5
isbn_softcover978-3-7643-8904-8
isbn_ebook978-3-7643-8905-5Series ISSN 1661-237X Series E-ISSN 2296-5041
issn_series 1661-237X
copyrightBirkh?user Basel 2009
The information of publication is updating

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Hidden Variables,tandard asymptotic theory usually does not apply because of model singularities. For example, we saw in Chapter 2 that the chi-square asymptotics for the likelihood ratio test are typically not valid at a singular point.
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Markov Bases,ency tables can be tested in an exact approach by performing random walks on a constrained set of tables with non-negative integer entries. Markov bases are of key importance to this statistical methodology because they comprise moves between tables that ensure that the random walk connects every pair of tables in the considered set.
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Bayesian Integrals,aximizing the likelihood function, we now seek to integrate that same function. This chapter aims to show how algebraic methods can be applied to various aspects of this problem. Section 5.1 discusses asymptotics of Bayesian integrals for large sample size, while Section 5.2 concerns exact evaluation of integrals for small sample size.
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itself one of the most profound questions ever faced by scie.The dark matter problem is one of the most fundamental and profoundly difficult to solve problems in the history of science. Not knowing what makes up most of the known universe goes to the heart of our understanding of the Universe and ou
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