Bayesian Data Analysis for Animal Scientists pp 213-246 | Cite as

# Model Selection

## Abstract

In Chaps. 6 and 7, before making inferences about parameters of interest (e.g. before comparing treatments), we wrote a model containing ‘noise’ effects 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 a better model could have been used to get better inferences. We can also think that we have underestimated our uncertainty, since we have some uncertainty about which is the best model for our inferences that we have not taken into account. The first problem is the goal of this chapter: how to choose the best model. The second problem has a difficult solution, since we cannot take into account all possible models to describe a natural phenomenon.

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