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## Abstract

In this paper, we propose an original approach to the solution of Fredholm equations of the second kind. We interpret the standard Von Neumann expansion of the solution as an expectation with respect to a probability distribution defined on a union of subspaces of variable dimension. Based on this representation, it is possible to use trans-dimensional Markov chain Monte Carlo (MCMC) methods such as Reversible Jump MCMC to approximate the solution numerically. This can be an attractive alternative to standard Sequential Importance Sampling (SIS) methods routinely used in this context. To motivate our approach, we sketch an application to value function estimation for a Markov decision process. Two computational examples are also provided.

Translated title of the contribution | On solving integral equations using Markov chain Monte Carlo methods |
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Original language | English |

Pages (from-to) | 2869 - 2880 |

Number of pages | 12 |

Journal | Applied Mathematics and Computation |

Volume | 216 |

Issue number | 10 |

DOIs | |

Publication status | Published - 15 Jul 2010 |

### Bibliographical note

Publisher: Elsevier## Fingerprint Dive into the research topics of 'On solving integral equations using Markov chain Monte Carlo methods'. Together they form a unique fingerprint.

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