Bayesian Inference

Bayes' Theorem is a mathematical formula used to calculate conditional probabilities, fundamental in statistics and machine learning.

The Bayesian Posterior is the updated probability of a hypothesis after observing evidence, central to Bayesian inference.

Degree of Belief quantifies the confidence in a statement based on evidence or experience.

Gibbs Sampling is a statistical technique for generating samples from a multivariate probability distribution.

A method for approximating complex probability distributions using a simpler normal distribution.

Likelihood-Free Inference estimates model parameters without explicitly calculating likelihoods, often using simulations.

Marginal likelihood is the probability of observing data under a model, integrating over all possible parameter values.

A statistical method using random sampling to estimate complex distributions.

Maximum A Posteriori (MAP) is a statistical method for estimating an unknown quantity by maximizing the posterior distribution.