One of the most attractive characteristics of Bayesian theory is the possibility of integrating prior information in the inferences; thus, we should discuss 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 due to the difficulty in properly integrating prior information in the analyses. It has also been stressed the impossibility of making inferences using probabilities when we have a total prior ignorance. This chapter is dedicated to discuss these topics, to see how and when Bayesian inference can use prior information and how we can deal with the problem of not having any prior information that we would like to consider.
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