Neural Comput - Markov chain Monte Carlo methods for state-space models with point process observations.

Tópicos

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{ featur(1941) imag(1645) propos(1176) }
{ perform(999) metric(946) measur(919) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ signal(2180) analysi(812) frequenc(800) }
{ result(1111) use(1088) new(759) }
{ clinic(1479) use(1117) guidelin(835) }
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{ data(1737) use(1416) pattern(1282) }
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{ take(945) account(800) differ(722) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
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{ can(981) present(881) function(850) }
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{ structur(1116) can(940) graph(676) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.

Resumo Limpo

letter consid number modern markov chain mont carlo mcmc method can appli paramet estim infer statespac model point process observ quantifi effici mcmc method synthet data result suggest reimannian manifold hamiltonian mont carlo method offer best perform compar method previous test variat bay method two experiment data set result indic similar perform larg data set superior perform small one work offer extens suit mcmc algorithm evalu import class model physiolog signal analysi

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