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Handbook of Approximate Bayesian Computation

first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and;

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Statistics for Astrophysics

Computation (ABC) and Bayesian nonparametric inference and clustering.;

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Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands;

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Essential Bayesian Models

This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook;

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Bayesian Statistics An Introduction 4th

computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics;

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New Developments in Computer Research

bayesian computation; broadband infrastructure programs in the American Recovery and Reinvestment Act and spectrum policy in the age of broadband.;

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Modelling Proteasome Dynamics in a Bayesian Framework

, is calibrated to experimental data from different proteasome isoforms using an approximate Bayesian computation approach. The dynamics of proteasome;

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Likelihood Free Methods for Cognitive Science

This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the;

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Bayesian Thinking, Modeling and Computation

This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application;

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Modeling and Reasoning with Bayesian Networks

This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive;

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Modeling and Reasoning with Bayesian Networks

This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive;

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Handbook of Bayesian Variable Selection

The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Current Trends in Bayesian Methodology with Applications

, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural;

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Current Trends in Bayesian Methodology with Applications

, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural;

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Bayesian Missing Data Problems

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;

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Bayesian Missing Data Problems

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;

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Computational Bayesian Statistics

a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion;

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Computational Bayesian Statistics

a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion;

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An Introduction to Bayesian Inference, Methods and Computation

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy;

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Bayesian Methods in Health Economics

statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness;

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Bayesian Methods in Health Economics

statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness;

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Statistical Modeling and Computation

Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an;

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Structured Matrix Based Methods for Approximate Polynomial GCD

computation. The first part of this book reviews the main results that have been proposed so far in the literature. As usual with polynomial;

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Handbook of Parallel Computing and Statistics

cumulants, data mining, econometrics, and Bayesian computation. Expert contributors summarize recent results and explore new directions in these;

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Bayesian Speech & Language Processing

With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems;

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