Unbiased Bayesian inference for population Markov jump processes via random truncations.
Unbiased Bayesian inference for population Markov jump processes via random truncations.
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DOI：
10.1007/s11222-016-9667-9
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发表时间：
2017
影响因子：
2.2
通讯作者：
Sanguinetti G
中科院分区：
文献类型：
--
作者：
Georgoulas A;Hillston J;Sanguinetti G
我们考虑连续时间马尔可夫过程中的人口的个体代理相互作用随机根据动力学规则。尽管这些模型在从生物学到智慧城市等领域越来越突出，但对这些系统的贝叶斯推断仍然具有挑战性，因为这些是具有潜在无限状态空间的连续时间离散状态系统。在这里，我们提出了一种新的有效的算法，联合状态/参数后验抽样人口马尔可夫跳跃过程。我们介绍了一类伪边缘采样算法的基础上的随机截断方法，使无限状态空间的原则性治疗。大量的基准模型的广泛评估表明，这种方法实现了相当大的节省相比，最先进的方法，保持精度和快速收敛。我们还介绍了合成生物学数据集的结果，显示了我们的工作的实际用途的潜力。
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose a novel efficient algorithm for joint state/parameter posterior sampling in population Markov Jump processes. We introduce a class of pseudo-marginal sampling algorithms based on a random truncation method which enables a principled treatment of infinite state spaces. Extensive evaluation on a number of benchmark models shows that this approach achieves considerable savings compared to state of the art methods, retaining accuracy and fast convergence. We also present results on a synthetic biology data set showing the potential for practical usefulness of our work.