Monte Carlo Methods for Adaptive Disorder Problems

4Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.
Get full text

Abstract

We develop a Monte Carlo method to solve continuous-time adaptive disorder problems. An unobserved signal X undergoes a disorder at an unknown time to a new unknown level. The controller's aim is to detect and identify this disorder as quickly as possible by sequentially monitoring a given observation process Y. We adopt a Bayesian setup that translates the problem into a two-step procedure of (1) stochastic filtering followed by (2) an optimal stopping objective. We consider joint Wiener and Poisson observation processes Y and a variety of Bayes risk criteria. Due to the general setting, the state of our model is the full infinite-dimensional posterior distribution of X. Our computational procedure is based on combining sequential Monte Carlo filtering procedures with the regression Monte Carlo method for high-dimensional optimal stopping problems. Results are illustrated with several numerical examples. © Springer-Verlag Berlin Heidelberg 2012.

Cite

CITATION STYLE

APA

Ludkovski, M. (2012). Monte Carlo Methods for Adaptive Disorder Problems. In Springer Proceedings in Mathematics (Vol. 12, pp. 83–112). https://doi.org/10.1007/978-3-642-25746-9_3

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free