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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 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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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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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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Statistical Computation for Environmental Sciences in R

. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses;

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Bayesian Disease Mapping

, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the;

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Bayesian Disease Mapping

, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the;

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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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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data Bayesian Computation and goodness-of-fit;

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Bayesian Model Comparison

issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian;

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Models for Ecological Data

. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling;

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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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Bayesian Psychometric Modeling

Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces;

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Bayesian Psychometric Modeling

using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book;

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Bayesian Data Analysis

students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it;

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Bayesian Modeling Using WinBUGS

implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key;

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Probability and Bayesian Modeling

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus;

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Statistics for Astrophysics

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

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Disease Mapping

Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling;

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Advances in Artificial Intelligence

papers are organized in topical sections on Bayesian networks and uncertainty modeling; fuzzy logic and soft computing; knowledge representation;

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Extreme Value Modeling and Risk Analysis

, Bayesian inference, and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel;

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Bayesian Methods in Finance

Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to;

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Bayesian Item Response Modeling

statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly;

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