A methodological framework for Monte Carlo probabilistic inference for diffusion processes
A methodological framework for Monte Carlo probabilistic inference for diffusion processes
复制标题
扩散过程蒙特卡罗概率推理的方法框架
DOI：
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复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
O. Papaspiliopoulos
中科院分区：
文献类型：
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作者：
O. Papaspiliopoulos
本文中开发和审查的方法论框架涉及
对扩散过程的过渡密度的无偏蒙特卡洛估计，
扩散过程的确切模拟。前者与辅助变量有关
方法，它建立在无偏估计的丰富通用蒙特卡洛机械上
以及与所使用的技术有关的无限系列扩展的模拟
在各种科学领域，例如人口遗传学和运营研究。这
后者是在扩散的数字中最近的重大进步，它基于
所谓的扩散度量的Wiener-Poisson分解，并且具有有趣的
与布朗运动的确切模拟杀人时间的联系和
相互作用的粒子系统在本文中发现。具体应用
通过考虑
连续二零非线性过滤问题。
The methodological framework developed and reviewed in this article concerns the
unbiased Monte Carlo estimation of the transition density of a diffusion process, and
the exact simulation of diffusion processes. The former relates to auxiliary variable
methods, and it builds on a rich generic Monte Carlo machinery of unbiased estimation
and simulation of infinite series expansions which relates to techniques used
in diverse scientific areas such as population genetics and operational research. The
latter is a recent significant advance in the numerics for diffusions, it is based on the
so-called Wiener-Poisson factorization of the diffusion measure, and it has interesting
connections to exact simulation of killing times for the Brownian motion and
interacting particle systems, which are uncovered in this article. A concrete application
to probabilistic inference for diffusion processes is presented by considering
the continuous-discrete non-linear filtering problem.
DOI：
10.1007/s11009-007-9060-4
发表时间：
2008-03-01
影响因子：
0.9
作者：
Beskos, Alexandros;Papaspiliopoulos, Orniros;Roberts, Gareth O.
通讯作者：
Roberts, Gareth O.