Inference or model evaluation is the process of updating probabilities of the denouement derived from the model at the end. As a result, all the probabilistic evidence is eventually known against the observation at hand so that observations can be updated while using the Bayesian model for classification analysis. Later on, this information is fetched to the Bayesian model by instantiating the consistency against all the observations in the dataset. The rules that are fetched to the model are referred to as prior probabilities where a probability is assessed before making reference to certain relevant observations, especially subjectively or on the assumption that all possible outcomes be given the same probability. Then beliefs are computed when all the evidence is known as posterior probabilities. These posterior probabilities reflect the levels of hypothesis computed based on updated evidence.

The Bayes theorem is used to compute the posterior probabilities that signify a consequence of two antecedents. Based on these antecedents, a prior probability and a likelihood function are derived from a statistical model for the new data for model adaptability. We will further discuss the Bayes theorem in a later section.