Estimating parameters in stochastic compartmental models using Markov chain methods
Estimating parameters in stochastic compartmental models using Markov chain methods
复制标题
使用马尔可夫链方法估计随机房室模型中的参数
DOI：
10.1093/imammb/15.1.19
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发表时间：
1998
影响因子：
1.1
通讯作者：
E. Renshaw
中科院分区：
文献类型：
--
作者：
G. Gibson;E. Renshaw
本文提出了一种利用马尔可夫过程的不完全观测值估计随机房室模型参数的马尔可夫链蒙特卡罗方法。的方法，这是基于Metropolis-Hastings算法，开发的背景下，流行病模型。它们的使用说明了特定的情况下，只有易感，感染，并删除状态表示使用模拟实现的过程。通过比较估计的似然与理论形式，在这些情况下，可以得出，或与已知的模型参数，我们表明，该方法可以用来提供有意义的估计参数和参数的不确定性。还讨论了这些技术的潜在应用。
Markov chain Monte Carlo methodology is presented for estimating parameters in stochastic compartmental models from incomplete observations of the corresponding Markov process. The methods, which are based on the Metropolis-Hastings algorithm, are developed in the context of epidemic models. Their use is illustrated for the particular case where only susceptible, infective, and removed states are represented using simulated realizations of the process. By comparing estimated likelihoods with theoretical forms, in cases where these can be derived, or with the known model parameters, we show that the methods can be used to provide meaningful estimates of parameters and parameter uncertainty. Potential applications of the techniques are also discussed.