While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for ...
Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a ...
Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been sh...
Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, t...
Max Henrion, Malcolm Pradhan, Brendan Del Favero, ...
The increasing number of knowledge-based systems that build on a Bayesian belief network or influence diagram acknowledge the usefulness of these frameworks for addressing complex...
In non-ergodic belief networks the posterior belief of many queries given evidence may become zero. The paper shows that when belief propagation is applied iteratively over arbitr...
The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with con...
Abstract. In this paper, we present a framework for supporting intelligent fault and performance management for communication networks. Belief networks are taken as the basis for k...
In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks...