Tensor product algorithms for inference of contact network from epidemiological data

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatoria...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 25; no. 1; pp. 285 - 26
Main Authors:	Dolgov, Sergey, Savostyanov, Dmitry
Format:	Journal Article
Language:	English
Published:	London BioMed Central 02.09.2024 Springer Nature B.V BMC
Subjects:	Algorithms Approximation Bayes Theorem Bayesian analysis Bayesian inference Bioinformatics Biomedical and Life Sciences Black boxes Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer Simulation Datasets Epidemics Epidemiological modelling Epidemiology Humans Inverse problems Life Sciences Markov analysis Markov chain Monte Carlo Mathematical models Microarrays Networks Optimization Ordinary differential equations Probability Probability distribution Probability theory Statistical inference Stochastic simulation algorithm Tensor train Tensors Networks 37N25 15A69 Tensor train 65F55 90B15 Markov chain Monte Carlo Epidemiological modelling 60J28 95C42 34A30 Stochastic simulation algorithm Bayesian inference 62F15
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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