Prior predictive checks are a method used in Bayesian statistics to evaluate the plausibility of a model by generating data from the prior predictive distribution. This process helps to assess whether the chosen priors and the model can produce data that resembles the observed data, thereby ensuring the model's adequacy before analyzing actual data. It connects to Bayesian inference and decision making by allowing statisticians to critically examine their assumptions and make adjustments if necessary.
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