標(biāo)題: Titlebook: Bayesian Data Analysis for Animal Scientists; The Basics Agustín Blasco Textbook 2017 Springer International Publishing AG 2017 Bayesian st [打印本頁] 作者: lutein 時(shí)間: 2025-3-21 16:03
書目名稱Bayesian Data Analysis for Animal Scientists影響因子(影響力)
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書目名稱Bayesian Data Analysis for Animal Scientists網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Bayesian Data Analysis for Animal Scientists讀者反饋
書目名稱Bayesian Data Analysis for Animal Scientists讀者反饋學(xué)科排名
作者: NUDGE 時(shí)間: 2025-3-21 20:42 作者: GRIN 時(shí)間: 2025-3-22 03:57
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.作者: 嗎啡 時(shí)間: 2025-3-22 06:50
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..作者: Ebct207 時(shí)間: 2025-3-22 12:32
,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.作者: critic 時(shí)間: 2025-3-22 12:56 作者: HOWL 時(shí)間: 2025-3-22 20:31 作者: 圣人 時(shí)間: 2025-3-23 00:03
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.作者: IRATE 時(shí)間: 2025-3-23 03:00 作者: companion 時(shí)間: 2025-3-23 08:56 作者: 北極熊 時(shí)間: 2025-3-23 12:46 作者: 確保 時(shí)間: 2025-3-23 17:01
G. Trendafiloski,M. Wyss,P. Rosset we will do, however, is to carry out a close examination to some difficult problems and outline their Bayesian solution. We have chosen three examples that have no solution using classical statistics, or for which the classical solution is not straightforward.作者: 迅速飛過 時(shí)間: 2025-3-23 19:21 作者: exophthalmos 時(shí)間: 2025-3-24 01:21 作者: ALE 時(shí)間: 2025-3-24 06:11
A Scope of the Possibilities of Bayesian Inference + MCMC, we will do, however, is to carry out a close examination to some difficult problems and outline their Bayesian solution. We have chosen three examples that have no solution using classical statistics, or for which the classical solution is not straightforward.作者: 起波瀾 時(shí)間: 2025-3-24 07:52 作者: Aphorism 時(shí)間: 2025-3-24 13:57 作者: Lacunar-Stroke 時(shí)間: 2025-3-24 17:10 作者: debris 時(shí)間: 2025-3-24 21:03 作者: OCTO 時(shí)間: 2025-3-25 02:03
Do We Understand Classic Statistics?,stimators, maximum likelihood, etc., and we examine the most common misunderstandings about them. We will see the limitations of classical statistics in order to stress the advantages of using Bayesian procedures in the following chapters.作者: cortex 時(shí)間: 2025-3-25 04:26
The Bayesian Choice,nferences. We introduce new tools as the probability of relevance or the guaranteed value at a given probability. We will see the advantages of comparing treatments using ratios instead of differences. We will learn one of the main advantages of Bayesian procedures, the possibility of marginalisatio作者: Mirage 時(shí)間: 2025-3-25 11:19
The Baby Model,distributed and we only have to estimate the mean and variance of the Normal distribution. In Chaps. ., . and ., we will see models that are more complex. We will find first analytical solutions to understand better the meaning of conditional and marginal distributions, and we will use Gibbs samplin作者: Arroyo 時(shí)間: 2025-3-25 14:28
,The Linear Model: I. The ‘Fixed Effects’ Model,f variance and covariance. We will see what in a frequentist context, to a ‘fixed effects model.’ We discussed in Chap. . the differences between ‘fixed’ and ‘random’ effects in a classical context. In a Bayesian context, all effects are random because in a Bayesian context, uncertainty is described作者: Allowance 時(shí)間: 2025-3-25 17:53
,The Linear Model: II. The ‘Mixed’ Model,n Chap. ., Sect. 1.5, we have explained the differences between fixed and random effects in a frequentist context. However, as we said in Chap. ., in a Bayesian context, all effects are random; thus, there is no distinction between fixed models, random models or mixed models. Nevertheless, we keep t作者: Blasphemy 時(shí)間: 2025-3-25 22:31 作者: 忘恩負(fù)義的人 時(shí)間: 2025-3-26 03:58
Prior Information,iscuss here why this is so rarely done, at least in the biological application. Hitherto, we have assumed that prior distributions were flat or they had a convenient conjugated form, but we have derived the discussion about prior information to this chapter. Bayesian inference has been questioned du作者: 廢墟 時(shí)間: 2025-3-26 04:24
Model Selection,ts and effects of interest and we described which was the prior information of these effects, or in a frequentist context whether they were ‘fixed’ or ‘random’. We have assumed we know the right model without discussing whether there was a more appropriate model for our inferences. We can think that作者: 禁止,切斷 時(shí)間: 2025-3-26 09:36
Human Capital in the Middle Eaststimators, maximum likelihood, etc., and we examine the most common misunderstandings about them. We will see the limitations of classical statistics in order to stress the advantages of using Bayesian procedures in the following chapters.作者: Anonymous 時(shí)間: 2025-3-26 15:26 作者: 衰弱的心 時(shí)間: 2025-3-26 18:54
Human Casualties in Earthquakesdistributed and we only have to estimate the mean and variance of the Normal distribution. In Chaps. ., . and ., we will see models that are more complex. We will find first analytical solutions to understand better the meaning of conditional and marginal distributions, and we will use Gibbs samplin作者: 包裹 時(shí)間: 2025-3-26 22:29
G. Trendafiloski,M. Wyss,P. Rossetf variance and covariance. We will see what in a frequentist context, to a ‘fixed effects model.’ We discussed in Chap. . the differences between ‘fixed’ and ‘random’ effects in a classical context. In a Bayesian context, all effects are random because in a Bayesian context, uncertainty is described作者: 外面 時(shí)間: 2025-3-27 03:49
Human Casualties in Earthquakesn Chap. ., Sect. 1.5, we have explained the differences between fixed and random effects in a frequentist context. However, as we said in Chap. ., in a Bayesian context, all effects are random; thus, there is no distinction between fixed models, random models or mixed models. Nevertheless, we keep t作者: 流浪者 時(shí)間: 2025-3-27 06:50
G. Trendafiloski,M. Wyss,P. Rossetis for the standard linear model, including mixed models. We have faced common problems like comparison among treatments, regression and covariates, genetic merit prediction, variance components estimation and so on. Now we will try to see some of the possibilities of Bayesian analyses in models tha作者: 猜忌 時(shí)間: 2025-3-27 12:05 作者: insipid 時(shí)間: 2025-3-27 14:12
James M. Kozlowski,Julia A. Sensibarts and effects of interest and we described which was the prior information of these effects, or in a frequentist context whether they were ‘fixed’ or ‘random’. We have assumed we know the right model without discussing whether there was a more appropriate model for our inferences. We can think that作者: 精美食品 時(shí)間: 2025-3-27 19:29
https://doi.org/10.1007/978-3-319-54274-4Bayesian statistics; Animal production; Animal breeding; Biostatistics; MCMC, Monte-Carlo Markov Chain m作者: 失誤 時(shí)間: 2025-3-28 01:55 作者: 租約 時(shí)間: 2025-3-28 02:37
Do We Understand Classic Statistics?,stimators, maximum likelihood, etc., and we examine the most common misunderstandings about them. We will see the limitations of classical statistics in order to stress the advantages of using Bayesian procedures in the following chapters.作者: scrutiny 時(shí)間: 2025-3-28 08:58
Human Capital in the Middle Eaststimators, maximum likelihood, etc., and we examine the most common misunderstandings about them. We will see the limitations of classical statistics in order to stress the advantages of using Bayesian procedures in the following chapters.作者: 洞察力 時(shí)間: 2025-3-28 10:34
