An efficient method to simulate diffusion bridges

IF 1.6 2区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Statistics and Computing Pub Date : 2024-06-12 DOI:10.1007/s11222-024-10439-z
H. Chau, J. L. Kirkby, D. H. Nguyen, D. Nguyen, N. Nguyen, T. Nguyen
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Abstract

In this paper, we provide a unified approach to simulate diffusion bridges. The proposed method covers a wide range of processes including univariate and multivariate diffusions, and the diffusions can be either time-homogeneous or time-inhomogeneous. We provide a theoretical framework for the proposed method. In particular, using the parametrix representations we show that the approximated probability transition density function converges to that of the true diffusion, which in turn implies the convergence of the approximation. Unlike most of the methods proposed in the literature, our approach does not involve acceptance-rejection mechanics. That is, it is acceptance-rejection free. Extensive numerical examples are provided for illustration and demonstrate the accuracy of the proposed method.

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模拟扩散桥的高效方法
本文提供了一种模拟扩散桥的统一方法。所提出的方法涵盖了包括单变量和多变量扩散在内的多种过程,扩散可以是时间均质的,也可以是时间非均质的。我们为提出的方法提供了一个理论框架。特别是,利用参数矩阵表示法,我们证明了近似概率过渡密度函数收敛于真实扩散的概率过渡密度函数,这反过来又意味着近似的收敛性。与文献中提出的大多数方法不同,我们的方法不涉及接受-拒绝力学。也就是说,它不涉及接受排斥。我们提供了大量的数值示例进行说明,并证明了所提方法的准确性。
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来源期刊
Statistics and Computing
Statistics and Computing 数学-计算机:理论方法
CiteScore
3.20
自引率
4.50%
发文量
93
审稿时长
6-12 weeks
期刊介绍: Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.
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