Partially observed optimal stopping problem for discrete-time Markov processes
DE SAPORTA, Benoîte
Quality control and dynamic reliability [CQFD]
Institut Montpelliérain Alexander Grothendieck [IMAG]
Quality control and dynamic reliability [CQFD]
Institut Montpelliérain Alexander Grothendieck [IMAG]
DUFOUR, François
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
NIVOT, Christophe
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
DE SAPORTA, Benoîte
Quality control and dynamic reliability [CQFD]
Institut Montpelliérain Alexander Grothendieck [IMAG]
Quality control and dynamic reliability [CQFD]
Institut Montpelliérain Alexander Grothendieck [IMAG]
DUFOUR, François
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
NIVOT, Christophe
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
< Reduce
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Language
en
Article de revue
This item was published in
4OR: A Quarterly Journal of Operations Research. 2017, vol. 15, p. 277-302
Springer Verlag
English Abstract
This paper is dedicated to the investigation of a new numerical method to approximate the optimal stopping problem for a discrete-time continuous state space Markov chain under partial observations. It is based on a two-step ...Read more >
This paper is dedicated to the investigation of a new numerical method to approximate the optimal stopping problem for a discrete-time continuous state space Markov chain under partial observations. It is based on a two-step discretization procedure based on optimal quantization. First,we discretize the state space of the unobserved variable by quantizing an underlying reference measure. Then we jointly discretize the resulting approximate filter and the observation process. We obtain a fully computable approximation of the value function with explicit error bounds for its convergence towards the true value fonction.Read less <
Origin
Hal imported