Probabilistic Models

A belief network is a graphical model that represents probabilistic relationships among variables.

A Beta Distribution Prior is a statistical model used in Bayesian statistics to represent beliefs about probabilities.

Exact Inference is a statistical method that calculates the exact probabilities of outcomes in a probabilistic model.

The Forward-Forward Algorithm is a technique used in Hidden Markov Models for computing probabilities of sequences.

Graphical models are probabilistic models that represent complex relationships using graphs.

Gumbel Softmax is a technique for differentiable sampling from categorical distributions in machine learning.

A Markov Model is a statistical model that predicts future states based solely on the current state, without memory of past states.

A Markov Random Field (MRF) is a graphical model that represents the joint distribution of a set of random variables with local dependencies.

A Mixture Density Network (MDN) predicts probability distributions instead of single outputs, useful for complex data modeling.

Output probability refers to the likelihood of a specific outcome in a probabilistic model or AI system.

Parameter Probability refers to the likelihood of specific model parameters given the observed data.