Markov Bridges, Bisection and Variance Reduction

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 23)

Abstract

Time-continuous Markov jump processes are popular modeling tools in disciplines ranging from computational finance and operations research to human genetics and genomics. The data is often sampled at discrete points in time, and it can be useful to simulate sample paths between the datapoints. In this paper we firstly consider the problem of generating sample paths from a continuous-time Markov chain conditioned on the endpoints using a new algorithm based on the idea of bisection. Secondly we study the potentials of the bisection algorithm for variance reduction. In particular, examples are presented where the methods of stratification, importance sampling and quasi Monte Carlo are investigated.

Keywords

Sample Path Importance Sampling Variance Reduction Rate Matrix Continuous Time Markov Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Mathematical SciencesAarhus UniversityAarhusDenmark
  2. 2.Bioinformatics Research CenterAarhus UniversityArhusDenmark

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