An information theoretic interface for a stochastic model management system

Abstract

Model management systems are intended to access, retrieve, and process models from a model base for a user in response to relatively simple commands and queries. For stochastic models a major problem is the transformation of problem specific information available to the decision maker into model parameters. The problem results from the various mixtures of incomplete, uncertain, and subjective data typical of stochastic environments. This paper proposes an interface for stochastic model management based on the information, theoretic principle of relative-entropy minimization. The Relative-Entropy Model Management System Interface (REMMSI) has the following key characteristics: (1) both subjective and objective data may be processed by the system, (2) parameter distributions are dynamically updated in the presence of new information, (3) inconsistent information provided by the user may be accepted and processed, and (4) the approach is applicable to a wide range of stochastic model management schemes.