AIC-PRAiSE

PRAiSE (for Probabilistic Reasoning As Symbolic Evaluation) is a library developed at SRI International's Artificial Intelligence Center, which implements the ideas presented in the following paper:

"Probabilistic Inference Modulo Theories",
de Salvo Braz, R., O'Reilly, C., Gogate, V., Dechter, R.,
Proc. of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16).
(revised version, original version, bibtex)

• propositional variables
• equality on categorical types
• equality and inequalities over bounded integers (more specifically, difference arithmetic).
• linear real arithmetic

• algebraic data types
• random functions (also described as relational variables, or uninterpreted functions - the type of inference common in the lifted first-order probabilistic inference literature).

• Experiment with the PRAiSE demo by downloading the jar file and running
  java -jar praise-gui.jar
  (this requires Java 8 to be installed)
• Download and run the batch mode command-line version with
  java -jar praise.jar
  (this requires Java to be installed)
  For quickly running an example, download earthquake.txt to the same directory and run
  java -jar praise.jar earthquake.txt --query alarm
  For more options, run
  java -jar praise.jar --help
• Read the User Guide
• Use the library through Maven in your own JVM projects (see "Latest Maven information" at "Getting Started")
• Install the project to use in your own JVM projects or become a contributor