Glossary
BN : Bayesian Network
UG : Undirected Graph
dBN : Dynamic Bayesian Network
DAG : Directed Acyclic Graph
PDAG : Partially Directed Acyclic Graph
EM : Expectation-Maximization algorithm, dealing with missng data
MLE : Maximum Likelihood Estimation
AIC : Akaike Information Criterion
BIC : Bayesian Information Criterion
O3PRM : Open Object Oriented Probabilistic Relational Model, Object oriented language for specification of PRM
MRF : Markov Random Field
ID : Influence Diagram
LIMID : Limited Memory Influence Diagram
CN : Credal Network
PRM : Probabilistic Relational Model
API : Application Programming Interface
Graphical model : A probabilistic model for which a graph expresses the conditional dependence structure between random variables.
Bayesian network : A probabilistic graphical model that represents a set of random variables and their conditional dependencies in the form of a directed acyclic graph (DAG).
Markov random field : A type of undirected graphical model that represents a set of random variables and their conditional dependencies in the form of an undirected graph (UG).
Influence diagram : A type of graphical model that represents a set of random variables and their conditional dependencies in the form of a directed acyclic graph.
Limited memory influence diagram : A type of influence diagram
Credal network : A type of graphical model that represents a set of random variables and their conditional dependencies in the form of a directed acyclic graph with sets of probability distributions.
Dynamic Bayesian network : A type of graphical model that represents a set of random variables and their conditional dependencies in the form of a directed acyclic graph that changes over (discrete) time. It is a generalisation of Markov Chain (with partial observation).
Probabilistic relational model : A type of graphical model that represents a set of random variables and their conditional dependencies using graphs and patterns (such as relational databases, or Object Oriented programming language).