A New Object-Oriented Stochastic Modeling Language.
Daniel J. Pless, George F. Luger, Carl R. Stern · 2000
A new language and inference algorithm for stochastic modeling is presented. This work refines and generalizes the stochastic functional language originally proposed by [1]. The language supports object-oriented representation and recursive functions. It provides a compact representation for a large class of stochastic models including infinite models. It provides the ability to represent general and abstract stochastic relationships and to decompose large models into smaller components. Our work extends the language of [1] by providing object encapsulation and reuse and a new and effective strategy for caching. An exact and complete inference algorithm is presented here that is expected to support efficient inference over important classes of models and queries.