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Titlebook: Bayesian Data Analysis for Animal Scientists; The Basics Agustín Blasco Textbook 2017 Springer International Publishing AG 2017 Bayesian st

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發(fā)表于 2025-3-21 16:03:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Bayesian Data Analysis for Animal Scientists
期刊簡(jiǎn)稱(chēng)The Basics
影響因子2023Agustín Blasco
視頻videohttp://file.papertrans.cn/182/181836/181836.mp4
發(fā)行地址An easy and intuitive introduction to Bayesian methods with Monte-Carlo Markov Chain methods (MCMC).The author uses colours to help the understanding of the formulae and numerous graphs to make the in
圖書(shū)封面Titlebook: Bayesian Data Analysis for Animal Scientists; The Basics Agustín Blasco Textbook 2017 Springer International Publishing AG 2017 Bayesian st
影響因子.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..
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Human Casualties in Earthquakese the same. In this chapter, we examine a common mixed model in animal production, the model with repeated records and the most widely used mixed model in genetic evaluation. We end the chapter with an introduction to multitrait models.
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Textbook 2017 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..
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,The Linear Model: II. The ‘Mixed’ Model,e the same. In this chapter, we examine a common mixed model in animal production, the model with repeated records and the most widely used mixed model in genetic evaluation. We end the chapter with an introduction to multitrait models.
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Vijay Pereira,Mark Neal,Wardah Qureshiing treatments using ratios instead of differences. We will learn one of the main advantages of Bayesian procedures, the possibility of marginalisation. We also will see some misinterpretations of Bayesian theory and procedures.
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