MVABN

MVABN is an acronym used in statistics and machine learning to denote a class of probabilistic graphical models that extend traditional Bayesian networks to handle multivariate dependencies among a set of variables. In a MVABN, variables are represented as nodes in a directed acyclic graph, and each node is associated with a conditional distribution given its parent variables.

The framework supports combining prior knowledge with data through Bayesian inference. Parameters are equipped with priors and inference yields posterior distributions. Structure learning seeks a graph that best explains the observed data according to a scoring rule or marginal likelihood.

Typical implementations use conditional distributions suitable for mixed data types: Gaussian or linear models for continuous variables, categorical/discrete models for discrete variables, and conditional Gaussian models for mixed continuous and discrete cases. Inference often relies on approximate methods such as Markov chain Monte Carlo or variational inference due to computational complexity.

Dynamic and multivariate extensions include dynamic MVABN for time-series, latent-variable MVABN for unobserved factors, and copula-based MVABN to capture nonlinear dependencies. These variants aim to improve modeling of complex systems in fields such as genomics, finance, engineering, and environmental science.

See also Bayesian network, probabilistic graphical model, and dynamic Bayesian network.