Changelog

Marginal inference: Runs MC-SAT to infer marginal probabilities.
Weight learning: A basic implementation of discriminative weight learning.
Soft evidence: Allows input evidence to be probabilistic.
Datalog: Besides MLN rules, you can now write deterministic rules evaluated in the Datalog way.
Predicate scoping: By default the ground atoms of a predicate are generated from the cross product of all predicate arguments, which is often unnecessary and sometimes explosive. This feature allows you to explicitly scope the set of ground atoms for each predicate.
Boolean expressions: In addtion to regular literals, now you can also write boolean expressions using built-in arithemetics or string operations.
Doing away with BDB: The dependency on Berkeley DB has been removed.

Added support for OpenBUGS as a model specification language [pre-alpha release]

Add-in support for Accumulo and HBase.

Elementary 0.1 (released Aug 2012)

This is the initial release of Elementary. Main features include:

Scalable single-machine Gibbs sampling: Takes advantage of hybrid storage manager to scale up problems that are larger than main memory.
Distant supervision: Performs distant supervision to combine learning and inference into a unified task.
MAP/marginal inference and weight learning: Support a set of tasks used in data analytics.