English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We consider chemical reaction networks modeled by a discrete state andcontinuous in time Markov process for the vector copy number of the species andprovide a novel particle filter method for state and parameter estimation basedon exact observation of some of the species in continuous time. The conditionalprobability distribution of the unobserved states is shown to satisfy a systemof differential equations with jumps. We provide a method of simulating aprocess that is a proxy for the vector copy number of the unobserved speciesalong with a weight. The resulting weighted Monte Carlo simulation is then usedto compute the conditional probability distribution of the unobserved species.We also show how our algorithm can be adapted for a Bayesian estimation ofparameters and for the estimation of a past state value based on observationsup to a future time.

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