In recent years variational methods have become a popular tool for approximate inference and learning in a wide variety of proba- bilistic models. For each new application, however, it is currently necessary (cid:12)rst to derive the variational update equations, and then to implement them in application-speci(cid:12)c code. Each of these steps is both time consuming and error prone. In this paper we describe a general purpose inference engine called VIBES (‘Variational Infer- ence for Bayesian Networks’) which allows a wide variety of proba- bilistic models to be implemented and solved variationally without recourse to coding. New models are speci(cid:12)ed either through a simple script or via a graphical interface analogous to a drawing package. VIBES then automatically generates and solves the vari- ational equations. We illustrate the power and (cid:13)exibility of VIBES using examples from Bayesian mixture modelling.

Probabilistic expert systems based on Bayesian networks require initial specification of both qualitative graphical structure and quantitative conditional probability assessments. As (possibly incomplete) data accumulate on real cases, the parameters of the system may adapt, but it is also essential that the initial specifications be monitored with respect to their predictive performance. A range of monitors based on standardized scoring rules that are designed to detect both qualitative and quantitative departures from the specified model is presented. A simulation study demonstrates the efficacy of these monitors at uncovering such departures.>

Abstract A directed acyclic graph or influence diagram is frequently used as a representation for qualitative knowledge in some domains in which expert system techniques have been applied, and conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. It is shown how one can introduce imprecision into such probabilities as a data base of cases accumulates. By exploiting the graphical structure, the updating can be performed locally, either approximately or exactly, and the setup makes it possible to take advantage of a range of well‐established statistical techniques. As examples we discuss discrete models, models based on Dirichlet distributions and models of the logistic regression type.