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Bayesian Regression Modeling with INLA

Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression;

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Bayesian Regression Modeling with INLA

Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression;

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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Review of basic Bayesian disease mapping models Spatio-temporal modeling with MCMC and INLA Special topics include;

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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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Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE;

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Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE;

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Bayesian inference with INLA

random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA;

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Bayesian inference with INLA

random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA;

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Spatial & Spatio Temporal Bayesian

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the;

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Modeling in Mathematics

representing noisy sounds with short-time sinusoids, robust Bayesian regularized estimation based on regression model and robust quadratic regression;

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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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Regression Modeling: Methods, Theory, and Computation with SAS

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;

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Regression Modeling: Methods, Theory, and Computation with SAS

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;

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Bayesian Ideas And Data Analysis

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and;

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Bayesian Modeling and Computation in Python

the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern;

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Applied Regression Modeling

Master the fundamentals of regression without learning calculus with this one-stop resource The newly and thoroughly revised 3rd;

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Bayesian Disease Mapping

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;

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Bayesian Disease Mapping

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;

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Bayesian Models for Astrophysical Data

range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book;

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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 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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An Introduction to Generalized Linear Models

multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian;

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Computational Bayesian Statistics

underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the;

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Computational Bayesian Statistics

underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the;

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Bayesian Statistics for the Social Sciences

Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and;

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