In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference. Read more... Abstract: In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference Front Matter ....Pages i-xviii Do We Understand Classic Statistics? (Agustín Blasco)....Pages 1-32 The Bayesian Choice (Agustín Blasco)....Pages 33-65 Posterior Distributions (Agustín Blasco)....Pages 67-84 MCMC (Agustín Blasco)....Pages 85-102 The Baby Model (Agustín Blasco)....Pages 103-118 The Linear Model: I. The ‘Fixed Effects’ Model (Agustín Blasco)....Pages 119-135 The Linear Model: II. The ‘Mixed’ Model (Agustín Blasco)....Pages 137-165 A Scope of the Possibilities of Bayesian Inference + MCMC (Agustín Blasco)....Pages 167-192 Prior Information (Agustín Blasco)....Pages 193-211 Model Selection (Agustín Blasco)....Pages 213-246 Back Matter ....Pages 247-275
