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Bayesian Networks
Directed graphical models representing conditional dependencies between random variables
Markov Networks
Undirected graphical models for representing symmetric dependencies between variables
Exact Inference
Algorithms that precisely compute probability distributions in belief networks
Approximate Inference
Probabilistic estimation methods when exact inference is computationally too expensive
Conditional Random Fields
Discriminative models for structured prediction based on Markov networks
Apprentissage des Paramètres
Estimation des probabilités conditionnelles à partir de données observées
Structure Learning
Automatic discovery of network topology from data
Monte Carlo Methods
Stochastic sampling techniques for probabilistic inference
Message Passing
Distributed algorithms propagating local information to perform global inference
Dynamic Bayesian Networks
Temporal extension of Bayesian networks for modeling time series
Influence Diagrams
Graphical models for decision making under uncertainty combining probabilities and utilities
Hybrid Graphical Models
Networks combining discrete and continuous variables with appropriate distributions
Inférence Causale
Utilisation des réseaux de croyance pour modéliser et raisonner sur les relations causales
Variational Likelihood
Optimization methods to approximate complex probability distributions
Factor Graphs
A unified representation of graphical models facilitating the implementation of inference algorithms