Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the effective implementation of the iterative proportional fitting procedure
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
A Prototypical System for Soft Evidential Update
Applied Intelligence
Belief Update in Bayesian Networks Using Uncertain Evidence
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
An approach to hybrid probabilistic models
International Journal of Approximate Reasoning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
On the revision of probabilistic beliefs using uncertain evidence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
A hybrid algorithm to compute marginal and joint beliefs in Bayesian networks and its complexity
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
SOFSEM'05 Proceedings of the 31st international conference on Theory and Practice of Computer Science
Variations over the message computation algorithm of lazy propagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Soft evidential update via Markov chain Monte Carlo inference
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Exploiting the probability of observation for efficient bayesian network inference
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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In this paper we analyze the performance of three algorithms for soft evidential update, in which a probability distribution represented by a Bayesian network is modified to a new distribution constrained by given marginals, and closest to the original distribution according to cross entropy. The first algorithm is a new and improved version of the big clique algorithm [1] that utilizes lazy propagation [2]. The second and third algorithm [3] are wrapper methods that convert soft evidence to virtual evidence, in which the evidence for a variable consists of a likelihood ratio. Virtual evidential update is supported in existing Bayesian inference engines, such as Hugin. To evaluate the three algorithms, we implemented BRUSE (Bayesian Reasoning Using Soft Evidence), a new Bayesian inference engine, and instrumented it. The resulting statistics are presented and discussed.