Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Approximate learning of dynamic models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Mini-buckets: a general scheme for generating approximations in automated reasoning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dynamic bayesian networks: a factored model of probabilistic dynamics
ATVA'12 Proceedings of the 10th international conference on Automated Technology for Verification and Analysis
Approximate solutions for factored Dec-POMDPs with many agents
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy
Pervasive and Mobile Computing
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The Factored Frontier (FF) algorithm is a simple approximate inference algorithm for Dynamic Bayesian Networks (DBNs). It is very similar to the fully factorized version of the Boyen-Koller (BK) algorithm, but instead of doing an exact update at every step followed by marginalisation (projection), it always works with factored distributions. Hence it can be applied to models for which the exact update step is intractable. We show that FF is equivalent to (one iteration of) loopy belief propagation (LBP) on the original DBN, and that BK is equivalent (to one iteration of) LBP on a DBN where we cluster some of the nodes. We then show empirically that by iterating more than once, LBP can improve on the accuracy of both FF and BK. We compare these algorithms on two real-world DBNs: the first is a model of a water treatment plant, and the second is a coupled HMM, used to model freeway traffic.