Algebraic and Geometric Methods in Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algebraic and Geometric Methods in Statistics write by Paolo Gibilisco. This book was released on 2010. Algebraic and Geometric Methods in Statistics available in PDF, EPUB and Kindle. An up-to-date account of algebraic statistics and information geometry, which also explores the emerging connections between these two disciplines.
Algebraic and Geometric Methods in Discrete Mathematics
Algebraic and Geometric Methods in Discrete Mathematics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algebraic and Geometric Methods in Discrete Mathematics write by Heather A. Harrington. This book was released on 2017-03-16. Algebraic and Geometric Methods in Discrete Mathematics available in PDF, EPUB and Kindle. This volume contains the proceedings of the AMS Special Session on Algebraic and Geometric Methods in Applied Discrete Mathematics, held on January 11, 2015, in San Antonio, Texas. The papers present connections between techniques from “pure” mathematics and various applications amenable to the analysis of discrete models, encompassing applications of combinatorics, topology, algebra, geometry, optimization, and representation theory. Papers not only present novel results, but also survey the current state of knowledge of important topics in applied discrete mathematics. Particular highlights include: a new computational framework, based on geometric combinatorics, for structure prediction from RNA sequences; a new method for approximating the optimal solution of a sum of squares problem; a survey of recent Helly-type geometric theorems; applications of representation theory to voting theory and game theory; a study of fixed points of tensors; and exponential random graph models from the perspective of algebraic statistics with applications to networks. This volume was written for those trained in areas such as algebra, topology, geometry, and combinatorics who are interested in tackling problems in fields such as biology, the social sciences, data analysis, and optimization. It may be useful not only for experts, but also for students who wish to gain an applied or interdisciplinary perspective.
Polyhedral and Algebraic Methods in Computational Geometry
Polyhedral and Algebraic Methods in Computational Geometry - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Polyhedral and Algebraic Methods in Computational Geometry write by Michael Joswig. This book was released on 2013-01-04. Polyhedral and Algebraic Methods in Computational Geometry available in PDF, EPUB and Kindle. Polyhedral and Algebraic Methods in Computational Geometry provides a thorough introduction into algorithmic geometry and its applications. It presents its primary topics from the viewpoints of discrete, convex and elementary algebraic geometry. The first part of the book studies classical problems and techniques that refer to polyhedral structures. The authors include a study on algorithms for computing convex hulls as well as the construction of Voronoi diagrams and Delone triangulations. The second part of the book develops the primary concepts of (non-linear) computational algebraic geometry. Here, the book looks at Gröbner bases and solving systems of polynomial equations. The theory is illustrated by applications in computer graphics, curve reconstruction and robotics. Throughout the book, interconnections between computational geometry and other disciplines (such as algebraic geometry, optimization and numerical mathematics) are established. Polyhedral and Algebraic Methods in Computational Geometry is directed towards advanced undergraduates in mathematics and computer science, as well as towards engineering students who are interested in the applications of computational geometry.
Algebraic Geometry and Statistical Learning Theory
Algebraic Geometry and Statistical Learning Theory - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algebraic Geometry and Statistical Learning Theory write by Sumio Watanabe. This book was released on 2009-08-13. Algebraic Geometry and Statistical Learning Theory available in PDF, EPUB and Kindle. Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.
An Introduction to Algebraic Statistics with Tensors
An Introduction to Algebraic Statistics with Tensors - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook An Introduction to Algebraic Statistics with Tensors write by Cristiano Bocci. This book was released on 2019-09-11. An Introduction to Algebraic Statistics with Tensors available in PDF, EPUB and Kindle. This book provides an introduction to various aspects of Algebraic Statistics with the principal aim of supporting Master’s and PhD students who wish to explore the algebraic point of view regarding recent developments in Statistics. The focus is on the background needed to explore the connections among discrete random variables. The main objects that encode these relations are multilinear matrices, i.e., tensors. The book aims to settle the basis of the correspondence between properties of tensors and their translation in Algebraic Geometry. It is divided into three parts, on Algebraic Statistics, Multilinear Algebra, and Algebraic Geometry. The primary purpose is to describe a bridge between the three theories, so that results and problems in one theory find a natural translation to the others. This task requires, from the statistical point of view, a rather unusual, but algebraically natural, presentation of random variables and their main classical features. The third part of the book can be considered as a short, almost self-contained, introduction to the basic concepts of algebraic varieties, which are part of the fundamental background for all who work in Algebraic Statistics.
