a parameter of probability distribution govern the probabilities associated with different conditions in that distribution. It is usually a vector: For instance, for uniform Uni(\alpha, \beta), parameter \theta = [\alpha, \beta]. importantly, for a discrete distribution system with 6 parameters, we only need 5 independent parameters to be able to satisfy the entire system. This is because a probability distribution must sum to 1. however, for a conditional probability:

we need to specificity (n-1)m parameters, whereby m is the number of states a can take, and n the number of states n can take. Each group of m has to add up to 1. parameter learning see parameter learning