Intelligent Probabilistic Inference

TL;DR

This paper explores influence diagrams as an intuitive, non-mathematical tool for representing probabilistic models, enabling efficient inference, sensitivity analysis, and decision-making by exploiting conditional independence.

Contribution

It introduces methods to determine arbitrary conditional probabilities from influence diagrams using local operations and qualitative dependence information.

Findings

Influence diagrams effectively represent probabilistic dependencies clearly.

Conditional probabilities can be derived using local computations over subspaces.

The approach facilitates decision-making with incomplete information.

Abstract

The analysis of practical probabilistic models on the computer demands a convenient representation for the available knowledge and an efficient algorithm to perform inference. An appealing representation is the influence diagram, a network that makes explicit the random variables in a model and their probabilistic dependencies. Recent advances have developed solution procedures based on the influence diagram. In this paper, we examine the fundamental properties that underlie those techniques, and the information about the probabilistic structure that is available in the influence diagram representation. The influence diagram is a convenient representation for computer processing while also being clear and non-mathematical. It displays probabilistic dependence precisely, in a way that is intuitive for decision makers and experts to understand and communicate. As a result, the same influence diagram can be used to build, assess and analyze a model, facilitating changes in the formulation and feedback from sensitivity analysis. The goal in this paper is to determine arbitrary conditional probability distributions from a given probabilistic model. Given qualitative information about the dependence of the random variables in the model we can, for a specific conditional expression, specify precisely what quantitative information we need to be able to determine the desired conditional probability distribution. It is also shown how we can find that probability distribution by performing operations locally, that is, over subspaces of the joint distribution. In this way, we can exploit the conditional independence present in the model to avoid having to construct or manipulate the full joint distribution. These results are extended to include maximal processing when the information available is incomplete, and optimal decision making in an uncertain environment. Influence diagrams as a computer-aided modeling tool were developed by Miller, Merkofer, and Howard [5] and extended by Howard and Matheson [2]. Good descriptions of how to use them in modeling are in Owen [7] and Howard and Matheson [2]. The notion of solving a decision problem through influence diagrams was examined by Olmsted [6] and such an algorithm was developed by Shachter [8]. The latter paper also shows how influence diagrams can be used to perform a variety of sensitivity analyses. This paper extends those results by developing a theory of the properties of the diagram that are used by the algorithm, and the information needed to solve arbitrary probability inference problems. Section 2 develops the notation and the framework for the paper and the relationship between influence diagrams and joint probability distributions. The general probabilistic inference problem is posed in Section 3. In Section 4 the transformations on the diagram are developed and then put together into a solution procedure in Section 5. In Section 6, this procedure is used to calculate the information requirement to solve an inference problem and the maximal processing that can be performed with incomplete information. Section 7 contains a summary of results.