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

cortex

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

Mirage

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

Arroyo

,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

Allowance

,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

Blasphemy

忘恩负义的人

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

废墟

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

禁止,切断

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.

Anonymous

衰弱的心

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