J. Comput. Biol. - Computational methods for a class of network models.

Tópicos

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{ patient(2837) hospit(1953) medic(668) }
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{ age(1611) year(1155) adult(843) }
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{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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{ first(2504) two(1366) second(1323) }
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{ use(1733) differ(960) four(931) }
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{ survey(1388) particip(1329) question(1065) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In the following article, we provide an exposition of exact computational methods to perform parameter inference from partially observed network models. In particular, we consider the duplication attachment model that has a likelihood function that typically cannot be evaluated in any reasonable computational time. We consider a number of importance sampling (IS) and sequential Monte Carlo (SMC) methods for approximating the likelihood of the network model for a fixed parameter value. It is well-known that, for IS, the relative variance of the likelihood estimate typically grows at an exponential rate in the time parameter (here this is associated with the size of the network); we prove that, under assumptions, the SMC method will have relative variance that can grow only polynomially. In order to perform parameter estimation, we develop particle Markov chain Monte Carlo algorithms to perform Bayesian inference. Such algorithms use the aforementioned SMC algorithms within the transition dynamics. The approaches are illustrated numerically.

Resumo Limpo

follow articl provid exposit exact comput method perform paramet infer partial observ network model particular consid duplic attach model likelihood function typic evalu reason comput time consid number import sampl sequenti mont carlo smc method approxim likelihood network model fix paramet valu wellknown relat varianc likelihood estim typic grow exponenti rate time paramet associ size network prove assumpt smc method will relat varianc can grow polynomi order perform paramet estim develop particl markov chain mont carlo algorithm perform bayesian infer algorithm use aforement smc algorithm within transit dynam approach illustr numer

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