The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood...

probability of the model configuration given the observations (i.e., the posterior probability). Bayes' theorem is named after the Reverend Thomas Bayes (/beɪz/)...

prescribes how to update the prior with new information to obtain the posterior probability distribution, which is the conditional distribution of the uncertain...

function and a prior probability, the interpretation of the addition of both shape parameters to be sample size = ν = α·Posterior + β·Posterior is only correct...

closely related to subjective probability, often called "Bayesian probability". Bayesian inference derives the posterior probability as a consequence of two...

Bayesian probabilist specifies a prior probability. This, in turn, is then updated to a posterior probability in the light of new, relevant data (evidence)...

intervals are typically used to characterize posterior probability distributions or predictive probability distributions. Their generalization to disconnected...

a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution. The MAP can...

or "probably not fair". Posterior probability density function, or PDF (Bayesian approach). Initially, the true probability of obtaining a particular...

its head Buttocks, as a euphemism Posterior horn (disambiguation) Posterior probability, the conditional probability that is assigned when the relevant...