The use of prior distributions is often a controversial topic in Bayesian inference. Informative priors are often avoided at all costs. However, when prior information is available, informative priors are appropriate means of introducing this information into the model. Furthermore, informative priors, when used properly and creatively, can provide solutions to computational issues and improve inference. Through 3 examples with different applications, we demonstrate the importance and utilities of informative priors in incorporating external information into the model and overcoming computational difficulties.
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