PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
A Model Checking Approach to the Parameter Estimation of Biochemical Pathways
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Some Investigations Concerning the CTMC and the ODE Model Derived From Bio-PEPA
Electronic Notes in Theoretical Computer Science (ENTCS)
Sliding Window Abstraction for Infinite Markov Chains
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
An anytime scheme for bounding posterior beliefs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Approximation of Event Probabilities in Noisy Cellular Processes
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
A Bayesian Approach to Model Checking Biological Systems
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Probabilistic Approximations of Signaling Pathway Dynamics
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
On the analysis of numerical data time series in temporal logic
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Probabilistic approximations of ODEs based bio-pathway dynamics
Theoretical Computer Science
A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
The factored frontier algorithm for approximate inference in DBNs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Analysis of signalling pathways using continuous time markov chains
Transactions on Computational Systems Biology VI
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Incremental signaling pathway modeling by data integration
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Rule-based modelling of cellular signalling
CONCUR'07 Proceedings of the 18th international conference on Concurrency Theory
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Dynamic Bayesian Networks (DBNs) can serve as succinct probabilistic dynamic models of biochemical networks [CHECK END OF SENTENCE]. To analyze these models, one must compute the probability distribution over system states at a given time point. Doing this exactly is infeasible for large models; hence one must use approximate algorithms. The Factored Frontier algorithm (FF) is one such algorithm [CHECK END OF SENTENCE]. However FF as well as the earlier Boyen-Koller (BK) algorithm [CHECK END OF SENTENCE] can incur large errors. To address this, we present a new approximate algorithm called the Hybrid Factored Frontier (HFF) algorithm. At each time slice, in addition to maintaining probability distributions over local states—as FF does—HFF explicitly maintains the probabilities of a number of global states called spikes. When the number of spikes is 0, we get FF and with all global states as spikes, we get the exact inference algorithm. We show that by increasing the number of spikes one can reduce errors while the additional computational effort required is only quadratic in the number of spikes. We validated the performance of HFF on large DBN models of biopathways. Each pathway has more than 30 species and the corresponding DBN has more than 3,000 nodes. Comparisons with FF and BK show that HFF is a useful and powerful approximate inferencing algorithm for DBNs.