State and parameter estimation from exact partial state observation in stochastic reaction networks

Simple item page
dc.contributor.authorRathinam, Muruhan
dc.contributor.authorYu, Mingkai
dc.date.accessioned2021-03-29T20:02:51Z
dc.date.available2021-03-29T20:02:51Z
dc.date.issued2020-12-09
dc.description.abstractWe consider chemical reaction networks modeled by a discrete state and continuous in time Markov process for the vector copy number of the species and provide a novel particle filter method for state and parameter estimation based on exact observation of some of the species in continuous time. The conditional probability distribution of the unobserved states is shown to satisfy a system of differential equations with jumps. We provide a method of simulating a process that is a proxy for the vector copy number of the unobserved species along with a weight. The resulting weighted Monte Carlo simulation is then used to compute the conditional probability distribution of the unobserved species. We also show how our algorithm can be adapted for a Bayesian estimation of parameters and for the estimation of a past state value based on observations up to a future time.en_US
dc.description.urihttps://arxiv.org/abs/2010.04346en_US
dc.format.extent27 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2zdss-7ypv
dc.identifier.citationRathinam, Muruhan; Yu, Mingkai; State and parameter estimation from exact partial state observation in stochastic reaction networks; Molecular Networks (2020); https://arxiv.org/abs/2010.04346en_US
dc.identifier.urihttp://hdl.handle.net/11603/21249
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectreaction networksen_US
dc.subjectmarkov processen_US
dc.subjectprobability distributionen_US
dc.titleState and parameter estimation from exact partial state observation in stochastic reaction networksen_US
dc.typeTexten_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Mathematics and Statistics Department
UMBC Faculty Collection
UMBC Student Collection