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Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC;

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Likelihood and Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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Likelihood & Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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Bayesian Analysis with Excel and R

of Excel's Bayesian capabilities and move toward R to do even more. Step by step, with real-world examples, Carlberg shows you how to use;

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Statistical Methods for QTL Mapping

comprehensive coverage of Bayesian models and MCMC algorithms and describes methods for multi-trait QTL mapping and eQTL mapping, including meta-trait;

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Bayesian Econometric Methods

Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and;

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Bayesian Econometric Methods

Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and;

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Bayesian Methods

become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral;

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Handbook of Markov Chain Monte Carlo

especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models;

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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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Likelihood Free Methods for Cognitive Science

This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the;

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Bayesian Evolutionary Analysis with BEAST

What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte;

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Bayesian Inference for Probabilistic Risk Assessment

of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who;

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Likelihood Methods in Biology and Ecology

in statistical theory and applications and discusses it in the context of biology and ecology. Bayesian and frequentist methods both use the likelihood;

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Likelihood Methods in Biology and Ecology

in statistical theory and applications and discusses it in the context of biology and ecology. Bayesian and frequentist methods both use the likelihood;

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Bayesian Methods in Health Economics

statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness;

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Bayesian Methods in Health Economics

statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness;

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Markov Chain Monte Carlo

understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and;

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Weighing the Odds

computing, and classical and modern statistical models. Statistics chapters present both the Frequentist and Bayesian approaches, emphasising;

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Weighing the Odds

computing, and classical and modern statistical models. Statistics chapters present both the Frequentist and Bayesian approaches, emphasising;

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Essentials of Statistical Inference

underlying the Bayesian, frequentist and Fisherian approaches, with particular emphasis on the contrasts between them. Computational ideas are;

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Essentials of Statistical Inference

underlying the Bayesian, frequentist and Fisherian approaches, with particular emphasis on the contrasts between them. Computational ideas are;

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Empirical Bayes and Likelihood Inference

Bayesian and such approaches to inference have a number of points of close contact, especially from an asymptotic point of view. Both;

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An Introduction to Bayesian Inference, Methods and Computation

and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods;

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MCMC from Scratch

This textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and;

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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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