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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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Essential Bayesian Models

of Statistics Series, with a focus on key methodologies and applications for Bayesian models and computation. It describes parametric and nonparametric;

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Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal;

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Categorical Data Analysis

Praise for the Second Edition A must-have book for anyone expecting to do research and/or applications in categorical data analysis;

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Statistical Methods for Categorical Data Analysis

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social;

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An Introduction to Categorical Data Analysis

A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically;

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Structural Equation Modeling

, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA;

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Bayesian Methods for Repeated Measures

the Bayesian analysis of categorical response data in a repeated measures study, Bayesian analysis for repeated measures when the mean profile;

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Bayesian Methods for Repeated Measures

the Bayesian analysis of categorical response data in a repeated measures study, Bayesian analysis for repeated measures when the mean profile;

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Cambridge Series in Statistical and Probabilistic Mathematics

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It;

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Cambridge Series in Statistical and Probabilistic Mathematics

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It;

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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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Bayesian Analysis with Python

data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian;

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Bayesian Models for Astrophysical Data

consistent focus is on hands-on modeling, analysis of data, and interpretation to address scientific questions. A must-have for astronomers, the;

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Regression Models for Categorical and Count Data

regression analysis on categorical and count data. The author covers both the theory and application of statistical models, with the help;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources;

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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

Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed;

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Graphical Models for Categorical Data

For advanced students of network data science, this compact account covers both well-established methodology and the theory of models;

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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

>Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interestWritten;

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Bayesian Analysis with Python

teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming;

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Bayesian Modeling in Bioinformatics

error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;

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Bayesian Modeling in Bioinformatics

error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;

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Bayesian Statistics for the Social Sciences

. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a;

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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

patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models;

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