Prior Samples

Context

• BayesiaLab can take into account Priors when estimating parameters using Maximum Likelihood Estimation.
• Priors reflect any a priori knowledge of an analyst regarding the domain, in other words, expert knowledge. See also Prior Knowledge for Structural Learning.
• These priors are expressed with an analyst-specified, initial Bayesian network (structure and parameters), plus analyst-specified Prior Samples.
• Prior Samples represent the analyst's subjective degree of confidence in the Priors.

where

• is the degree of confidence in the Prior.
• is the joint probability returned by the prior Bayesian network.
• BayesiaLab uses these two terms to generate virtual samples that are subsequently combined with the observed samples from the dataset.