P. Del Moral

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In this paper we introduce a class of non-linear Markov Chain Monte Carlo (MCMC) methods for simulating from a probability measure π. Non-linear Markov kernels (e.g. Del Moral (2004)) can be constructed to admit π as an invariant distribution and have typically superior mixing properties to ordinary (linear) MCMC kernels. However, such non-linear kernels(More)
The estimation of rare event probability is a crucial issue in areas such as reliability, telecommunications , aircraft management. In complex systems, analytical study is out of question and one has to use Monte Carlo methods. When rare is really rare, which means a probability less than 10 −9 , naive Monte Carlo becomes unreasonable. A widespread(More)
The aim of this paper is to present efficient algorithms for the detection of multiple targets in noisy images. The algorithms are based on the optimal filter of a multidi-mensional Markov chain signal. We also present some simulations, in the case of one, two and three targets, showing the efficiency of the method for detecting the positions of the targets.
The goal of this paper is to design a new control algorithm for open-loop control of complex systems. This control approach is based on a genealogical decision tree for both regulation and tracking control problems. The idea behind this control strategy consists of associating Gaussian distributions to both the norms of the control actions and the tracking(More)
Currently, 1000 whole human genomes are being sequenced. It is becoming exceedingly difficult to extract critical information from such extensive population-level genomic data reliably to solve pressing biomedical problems. Several approximate methods are being used without sound statistical justification to extract information from such complex data. This(More)
A new approach based on a genealogical decision tree is suggested for solving an open-loop tracking problem. The algorithm associates Gaussian distributions to both the norms of the control actions and the tracking errors. It solves the optimization problem sequentially, using random resampling from a population of solutions. This stochastic search model(More)
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