An exploration of Bayes Theorem, which establishes a relationship between conditional and marginal probabilities of random events, including historical context, types, applications, examples, and mathematical models.
A comprehensive guide on Markov Chain Monte Carlo (MCMC), a method for sampling from probability distributions, including historical context, types, key events, and detailed explanations.
An initial probability estimate before new evidence is considered (P(A)), crucial in Bayesian statistics and decision-making processes.
An in-depth analysis of posterior probability, its formulation and methods for calculation, and its applications in various fields such as Bayesian statistics, machine learning, and decision making.
