TY - JOUR
T1 - Anytime anyspace probabilistic inference
AU - Ramos, Fabio Tozeto
AU - Cozman, Fabio Gagliardi
PY - 2005/1/1
Y1 - 2005/1/1
N2 - This paper investigates methods that balance time and space constraints against the quality of Bayesian network inferences - we explore the three-dimensional spectrum of "time×space×quality" trade-offs. The main result of our investigation is the adaptive conditioning algorithm, an inference algorithm that works by dividing a Bayesian network into sub-networks and processing each sub-network with a combination of exact and anytime strategies. The algorithm seeks a balanced synthesis of probabilistic techniques for bounded systems. Adaptive conditioning can produce inferences in situations that defy existing algorithms, and is particularly suited as a component of bounded agents and embedded devices. © 2004 Elsevier Inc. All rights reserved.
AB - This paper investigates methods that balance time and space constraints against the quality of Bayesian network inferences - we explore the three-dimensional spectrum of "time×space×quality" trade-offs. The main result of our investigation is the adaptive conditioning algorithm, an inference algorithm that works by dividing a Bayesian network into sub-networks and processing each sub-network with a combination of exact and anytime strategies. The algorithm seeks a balanced synthesis of probabilistic techniques for bounded systems. Adaptive conditioning can produce inferences in situations that defy existing algorithms, and is particularly suited as a component of bounded agents and embedded devices. © 2004 Elsevier Inc. All rights reserved.
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U2 - 10.1016/j.ijar.2004.04.001
DO - 10.1016/j.ijar.2004.04.001
M3 - Article
SP - 53
EP - 80
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
SN - 0888-613X
ER -