Read Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101)

Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101)

Unknown Author

4.9/5 (21296 ratings)

Description:In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivariate complex normal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and parameter estimation and hypothesis testing for these models. After introducing undirected graphs, they then develop the theory of complex normal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101). To get started finding Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: —

Format: PDF, EPUB & Kindle Edition

Publisher: —

Release: —

ISBN: 1461242401

Description: In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivariate complex normal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and parameter estimation and hypothesis testing for these models. After introducing undirected graphs, they then develop the theory of complex normal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101). To get started finding Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Special Offer | $0.00

Special Offer! | $0.00

Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101)

Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics Book 101)

More Books