Sensitivities: an alternative to conditional probabilities for Bayesian belief networks

Authors:
Alexander V. Kozlov;Jaswinder Pal Singh
Affiliations:
Department of Applied Physics, Stanford University, Stanford, CA;Department of Computer Science, Princeton University, Princeton, NJ
Venue:
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Year:
1995

Citing 6
Cited 3

Evaluating influence diagrams

Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms

Probabilistic reasoning in expert systems: theory and algorithms
The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
A graph-based inference method for conditional independence

Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard

Artificial Intelligence

Overlap pattern synthesis with an efficient nearest neighbor classifier

Pattern Recognition
Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space

Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Computational complexity reduction for BN2O networks using similarity of states

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We show an alternative way of representing a Bayesian belief network by sensitivities and probability distributions. This representation is equivalent to the traditional representation by conditional probabilities, but makes dependencies between nodes apparent and intuitively easy to understand. We also propose a QR matrix representation for the sensitivities and/or conditional probabilities which is more efficient, in both memory requirements and computational speed, than the traditional representation for computer-based implementations of probabilistic inference. We use sensitivities to show that for a certain class of binary networks, the computation time for approximate probabilistic inference with any positive upper bound on the error of the result is independent of the size of the network. Finally, as an alternative to traditional algorithms that use conditional probabilities, we describe an exact algorithm for probabilistic inference that uses the QR-representation for sensitivities and updates probability distributions of nodes in a network according to messages from the neighbors.