Skip to Main content Skip to Navigation
Conference papers

Optimisation de plans d'expérience par méthodes particulaires

Abstract : We propose a new stochastic algorithm for Bayesian optimal design in nonlinear and high dimensional models. Like in the recent work of Peter Müller, we turn the optimization problem into a matter of Monte Carlo Markov chains simulations to explore the expected utility surface. The optimal design is then the mode of this surface seen as a probability distribution. Our algorithm mixes a "particles" method to efficiently explore high dimensional multimodal surfaces, with simulated annealing to concentrate the samples near the modes. We test it in a multiple change-point problem for time series data.
Document type :
Conference papers
Complete list of metadata
Contributor : Gestionnaire Civs Connect in order to contact the contributor
Submitted on : Thursday, April 3, 2014 - 4:28:31 PM
Last modification on : Tuesday, January 18, 2022 - 3:23:50 PM
Long-term archiving on: : Thursday, July 3, 2014 - 5:21:04 PM


Files produced by the author(s)


  • HAL Id : ineris-00972424, version 1
  • INERIS : PU-2003-060


Billy Amzal, Eric Parent, Frédéric Y. Bois, Christian P. Robert. Optimisation de plans d'expérience par méthodes particulaires. 35. Journées de Statistiques, Jun 2003, Lyon, France. pp.97-100. ⟨ineris-00972424⟩



Record views


Files downloads