Journal ArticleDOI
The Bayesian controversy in animal breeding.
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TLDR
Both Bayesian and frequentist schools of inference are established, and now neither of them has operational difficulties, with the exception of some complex cases.Abstract:
Frequentist and Bayesian approaches to scientific inference in animal breeding are discussed. Routine methods in animal breeding (selection index, BLUP, ML, REML) are presented under the hypotheses of both schools of inference, and their properties are examined in both cases. The Bayesian approach is discussed in cases in which prior information is available, prior information is available under certain hypotheses, prior information is vague, and there is no prior information. Bayesian prediction of genetic values and genetic parameters are presented. Finally, the frequentist and Bayesian approaches are compared from a theoretical and a practical point of view. Some problems for which Bayesian methods can be particularly useful are discussed. Both Bayesian and frequentist schools of inference are established, and now neither of them has operational difficulties, with the exception of some complex cases. There is software available to analyze a large variety of problems from either point of view. The choice of one school or the other should be related to whether there are solutions in one school that the other does not offer, to how easily the problems are solved, and to how comfortable scientists feel with the way they convey their results.read more
Citations
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Journal ArticleDOI
Estimating genetic parameters in natural populations using the 'animal model'
TL;DR: This work reviews the recent application of restricted maximum-likelihood "animal models" to multigenerational data from natural populations, and shows how the estimation of variance components and prediction of breeding values using these methods offer a powerful means of tackling the potentially confounding effects of environmental variation.
Journal ArticleDOI
The misuse of BLUP in ecology and evolution.
TL;DR: Analytically and through simulation and example why BLUP often gives anticonservative and biased estimates of evolutionary and ecological parameters is shown and how unbiased and powerful tests can be derived that adequately quantify uncertainty are shown.
Journal ArticleDOI
The History of Statistics. The Measurement of Uncertainty before 1900.
TL;DR: The History of StatisticsThe history of Statistics in the 17th and 18th CenturiesThe Politics of Large NumbersStatistics on the TableFiguring Out The PastDicing with DeathHow to Lie with StatisticsAnnotated Readings in the History of statistics.
Journal ArticleDOI
Probability, Statistics and Truth.
Journal ArticleDOI
Estimating evolutionary parameters when viability selection is operating
TL;DR: Using missing data theory, it is shown formally the conditions under which a valid evolutionary inference is possible when the invisible fraction and/or missing traits are ignored.
References
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Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
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Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.
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Monte Carlo Statistical Methods
TL;DR: This new edition contains five completely new chapters covering new developments and has sold 4300 copies worldwide of the first edition (1999).
Journal ArticleDOI
Bayesian Model Averaging: A Tutorial
TL;DR: Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty and provides improved out-of- sample predictive performance.