JarCastinggraphical-models
graphical-models is a Java library of exact and approximate inference algorithms in Bayesian and Markov networks.
Inference can be performed in networks with factors of types:
- Discrete factors
- Canonical Gaussian factors (including Gaussiand and conditional linear Gaussian distributions)
It provides implementations of the following algorithms:
- Variable Elimination
- Clique Tree Inference
- Loopy Belief Propagation
- Expectation Propagation
Networks are specified in terms of cluster graphs. A cluster, a node in the cluster graph, may consist of any number of factors.
Setup
Maven dependency:
<dependency>
<groupId>com.github.thorbenlindhauer</groupId>
<artifactId>graphmod-inference-engine</artifactId>
<version>0.1.0</version>
</dependency>
How to Use
Yet to come.
Variable Elimination
The strategy for determining the elimination order can be exchanged. The following default implementations exist:
- Min-fill strategy: Eliminate variables in the order that introduces the least number of fill edges to the factor graph
Clique Tree Inference
Implementations of the sum product and the belief update schema exist. Clique trees can be generated for a given factor graph based on a variable elimination strategy.
Loopy Belief Propagation
Strategies for defining message order and determining calibration in the graph can be exchanged.
Expectation Propagation
Expectation propagation is complementary to clique tree inference and loopy belief propagation. It serves as a mean for approximation when factor products cannot be performed in closed form. The strategy of approximating a set of factors that form a cluster is configurable and extensible. The following implementations exist:
- Univariate truncated Gaussians can be approximated in Gaussian networks
Defining Models
A fluent builder for defining factors, grouping factors to clusters, and creating a graph out of them exist. A rudimentary import for graphs specified in the XMLBIF format is provided in addition.
Reading
This library based on the concepts described in the book Probabilistic Graphical Models by Daphne Koller and Nir Friedmann:
Koller, Daphne, and Nir Friedman Probabilistic graphical models: principles and techniques MIT press, 2009