Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.
Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.
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通过线性噪声近似进行马尔可夫跳跃过程的马尔可夫链蒙特卡罗推理。
DOI：
10.1098/rsta.2011.0541
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Stathopoulos V
中科院分区：
文献类型：
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作者：
Stathopoulos V
马尔可夫跳变过程的贝叶斯分析是一个重要而富有挑战性的问题。虽然精确的推理在理论上是可能的，但它的计算要求很高，因此它的适用性仅限于一小类问题。在本文中，我们描述了黎曼流形马尔可夫链蒙特卡罗（MCMC）方法的应用，使用近似的MJP的可能性是有效的，当系统建模接近其热力学极限。该方法在统计和计算上都是高效的，而收敛速度和链的混合允许快速的MCMC推理。该方法是评估使用数值模拟两个问题从化学动力学和一个从系统生物学。
Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.