Genealogical particle analysis of rare events
By Pierre Del Moral, Josselin Garnier. 2005
161 citations recorded by the source corpus.
In this paper an original interacting particle system approach is developed for studying Markov chains in rare event regimes. The proposed particle system is theoretically studied through a genealogical tree interpretation of Feynman--Kac path measures. The algorithmic implementation of the particle system is presented. An estimator for the probability of occurrence of a rare event is proposed and its variance is computed, which allows to compare and to optimize different versions of the algorithm. Applications and numerical implementations are discussed. First, we apply the particle system technique to a toy model (a Gaussian random walk), which permits to illustrate the theoretical predictions. Second, we address a physically relevant problem consisting in the estimation of the outage probability due to polarization-mode dispersion in optical fibers.
Subfields: Ecology, Evolution, Behavior and Systematics, Electrical and Electronic Engineering, Plant Science
View Paper: https://www.semanticscholar.org/paper/433e38ad194268966d4e6697e2dc1da9e851935e
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In Ecology, Evolution, Behavior and Systematics
In Electrical and Electronic Engineering
- Lawrence M. Murray
- Olivier Capp'e
- Eric Moulines
- Pierre Del Moral
- R´emi Munos
- Tor Lattimore
- Navin Goyal
- Shipra Agrawal
In Plant Science
Citations in the corpus
- Sequential Monte Carlo with Highly Informative Observations (2014, methodology): “The methods proposed in this work are most similar to the SMC methods of Del Moral and Garnier [12] and Lin, Chen, and Mykland [27].”
- An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, background): “the event of interest is less likely than under the original distribution π ([115] use a classic exponential tilting to achieve this goal, whereas the construction of [116] makes a more thorough use of the Markov property).”