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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing

Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an;

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Resolving Spectral Mixtures

and multi-way analysis to Bayesian positive source separation and nonlinear unmixing. Unravelling total spectral data into the contributions;

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical;

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Multivariate Statistical Modelling Based on Generalized Linear Models

in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.;

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Applied Multivariate Analysis

discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and;

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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical;

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Bayesian Models for Categorical Data

, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to;

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Introduction to Bayesian Estimation and Copula Models of Dependence

, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management;

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Bayesian Analysis of Linear Models

in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text;

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Bayesian Analysis of Linear Models

in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text;

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A Course in Time Series Analysis

methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time;

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Bayesian Methods for Management and Business

Management and Business: Pragmatic Solutions for Real Problems demonstrates how Bayesian statistics can help to provide insights into important;

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Applied Bayesian Modelling

The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling;

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Bilinear Regression Analysis

is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis;

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Source Separation and Machine Learning

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS;

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Blind Identification and Separation of Complex-valued Signals

signals of interest called sources. They also consider the case of direct estimation of the inverse system for the purpose of source separation;

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Applied Multivariate Statistics with R

for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and;

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Introduction to Bayesian Statistics

statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for;

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Time Series

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling;

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Multivariate Statistics

The book aims to present a wide range of the newest results on multivariate statistical models, distribution theory and applications;

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Mathematical Statistics

the doctorate level. It covers estimation, prediction, testing, confidence sets, Bayesian analysis, and the general approach of decision;

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Latent Variable Analysis and Signal Separation

fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Audio Source Separation

factorization, deep neural networks, and sparse component analysis. The first section of the book covers single channel source separation based on non;

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Data Analysis and Signal Processing in Chromatography

manner applicable for enhancing not only chromatography, but also spectroscopic and other analytical signals* The first book with a thorough;

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Statistics of Extremes

last part of the book covers some interesting advanced topics, including time series, regression, multivariate and Bayesian modelling;

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Methods of Multivariate Statistics

-sided tests applicable in pharmaceutical and medical trials Bootstrap methods Principal component method for predicting a multivariate;

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