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Bayesian Models

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a;

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Benefits of Bayesian Network Models

The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A;

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Practical Applications of Bayesian Reliability

Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides;

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Risk Assessment and Decision Analysis with Bayesian Networks

Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on;

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Risk Assessment and Decision Analysis with Bayesian Networks

Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array;

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Bayesian Networks Handbook

A Bayesian network is also known as a Bayes network, belief network or causal probabilistic network. Bayesian belief networks are effective;

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Bayesian Networks for Reliability Engineering

This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN;

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Bayesian Computation with R

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase;

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Bayesian Analysis of Linear Models

With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date;

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Bayesian Analysis of Linear Models

With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date;

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Bayesian Analysis with Python

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian;

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Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

acquainted with Probability Theory to analyze random events * Build Linear Regression models * Use Bayesian networks to infer the probability;

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Bayesian Models for Categorical Data

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology;

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Bayesian Psychometric Modeling

analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples;

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Bayesian Psychometric Modeling

theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated;

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Bayesian Analysis with Python

Unleash the power and flexibility of the Bayesian framework About This Book * Simplify the Bayes process for solving complex;

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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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Bringing Bayesian Models to Life

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental;

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Bringing Bayesian Models to Life

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental;

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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 Models for Astrophysical Data

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to;

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Bayesian Analysis of Stochastic Process Models

Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified;

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Bayesian Artificial Intelligence

concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures;

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Bayesian Artificial Intelligence

concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures;

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Introduction to Bayesian Estimation and Copula Models of Dependence

Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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Data-Driven Computational Neuroscience

regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts;

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