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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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Bayesian Programming

financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an;

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Bayesian Programming

financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an;

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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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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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Bayesian Models

statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists;

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Subjective Probability Models for Lifetimes

Bayesian methods in reliability cannot be fully utilized and understood without full comprehension of the essential differences that exist;

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Subjective Probability Models for Lifetimes

Bayesian methods in reliability cannot be fully utilized and understood without full comprehension of the essential differences that exist;

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Advanced Methodologies for Bayesian Networks

reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the;

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Introductory Statistical Inference with the Likelihood Function

with sections on Bayesian computation and inference. An appendix contains unique coverage of the interpretation of probability, and coverage;

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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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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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Probability

of the growth of Bayesian Statistics. Personal probability is essential for modern Bayesian methods, and it can be difficult for students who;

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Biostatistics

confidence intervals, margins of error, and one-sided p-values-in Bayesian terms. Topics covered include: Probability and subjective probability;

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Uncertainty

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic;

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Uncertainty

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic;

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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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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 Essentials with R

introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not;

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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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Doing Bayesian Data Analysis

Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first;

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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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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition;

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Development of Clinical Decision Support Systems using Bayesian Networks

: in modeling and validation of workflows, guided modeling, semi-automated modeling, advanced Bayesian networks, model-user interaction, inter;

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