The paper addresses the estimation of abrupt changes which are contaminated by multiplicative Gaussian noise. The marginal mean a posteriori or marginal maximum a posteriori estimators can be derived for estimating the position of a single abrupt change. However, these estimators have optimization or integration problems for multiple abrupt changes. The paper solves these optimization problems by using Markov chain Monte Carlo methods (MCMC).
['Jean-Yves Tourneret', 'Michel Doisy', 'Manuel Mazzei']
Bayesian estimation of abrupt changes contaminated by multiplicative noise using MCMC