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Bayesian Model Selection and Statistical Modeling

Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model;

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Bayesian Model Selection and Statistical Modeling

different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors.;

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Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists

. However, many important contemporary methods of applied statistics, such as generalized linear modeling, mixed-effects modeling, and Bayesian;

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Bayesian Model Comparison

implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection;

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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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Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced;

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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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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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Maximum Entropy and Bayesian Methods

Mexico, USA, from July 31 to August 4, 1995. Maximum entropy and Bayesian methods are widely applied to statistical data analysis and scientific;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;

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Selection Models for Nonignorable Missing Data

models for nonignorable missing data. The theory of selection models is described and illustrated by examples. Maximum Likelihood as well as;

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Structural Equation Modeling

Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an;

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An Introduction to Generalized Linear Models

framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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An Introduction to Generalized Linear Models

model selection, and a Postface on good statistical practice. Like its predecessor, this edition presents the theoretical background;

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An Introduction to Generalized Linear Models

model selection, and a Postface on good statistical practice. Like its predecessor, this edition presents the theoretical background;

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Bayesian Models

enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management;

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Bayesian Cognitive Modeling

. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises;

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Bayesian Cognitive Modeling

. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises;

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Monte-Carlo Simulation-Based Statistical Modeling

general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available;

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Bayesian Phylogenetics

Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists;

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Bayesian Modeling Using WinBUGS

distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as;

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Modeling in Mathematics

Modeling in Mathematics deals with the concept of modeling in the field of mathematics. It includes a statistical and spectral model for;

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Circular Data Bayesian Modeling

statistical methods and models to circular data as well. In the first part of the book both the descriptive and probabilistic aspects of circular data;

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