Unbiased Bayesian inference for population Markov jump processes via random truncations

We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as thes...

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Detalles Bibliográficos
Autores principales: Georgoulas, Anastasis, Hillston, Jane, Sanguinetti, Guido
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477715/
https://www.ncbi.nlm.nih.gov/pubmed/28690370
http://dx.doi.org/10.1007/s11222-016-9667-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477715/
https://www.ncbi.nlm.nih.gov/pubmed/28690370
http://dx.doi.org/10.1007/s11222-016-9667-9

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