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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 Methods for Nonlinear Classification and Regression

Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian;

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Bayesian Hierarchical Models

and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal;

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Bayesian Hierarchical Models

data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal;

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Measurement, Regression, and Calibration

strongly, the latter allowing flexible analysis of a wide range of multivariate regression problems. The last chapter presents some Bayesian;

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Applied Bayesian Modelling

The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling;

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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

models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian;

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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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Case Studies in Bayesian Statistical Modelling and Analysis

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian;

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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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Bayesian Methods in Epidemiology

regression, and nonlinear models. The text also introduces a Bayesian approach for the estimation of survival by life tables and illustrates other;

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Bayesian Methods in Epidemiology

/ordinal regression, and nonlinear models. The text also introduces a Bayesian approach for the estimation of survival by life tables and;

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Bayesian Non- and Semi-parametric Methods and Applications

This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative;

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The BUGS Book

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the;

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The BUGS Book

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the;

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Probability and Bayesian Modeling

, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies;

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An Introduction to Bayesian Inference, Methods and Computation

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy;

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Bayesian Regression Modeling with INLA

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No;

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Bayesian Regression Modeling with INLA

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No;

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical;

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Data Analysis Using Hierarchical Generalized Linear Models with R

in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments;

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Data Analysis Using Hierarchical Generalized Linear Models with R

in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments;

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Bayesian Statistical Methods

. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized;

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Bayesian Statistical Methods

. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized;

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Machine Learning

squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It;

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Understanding Computational Bayesian Statistics

A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely;

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