Rare event simulation for steady-state probabilities via recurrency cycles
CHAOS, An Interdisciplinary Journal of Nonlinear Science , Volume 29 - Issue 3 p. 033131
We develop a new algorithm for the estimation of rare event probabilities associated with the steady-state of a Markov stochastic process with continuous state space Rd and discrete time steps (i.e., a discrete-time Rd-valued Markov chain). The algorithm, which we coin Recurrent Multilevel Splitting (RMS), relies on the Markov chain's underlying recurrent structure, in combination with the Multilevel Splitting method. Extensive simulation experiments are performed, including experiments with a nonlinear stochastic model that has some characteristics of complex climate models. The numerical experiments show that RMS can boost the computational efficiency by several orders of magnitude compared to the Monte Carlo method.
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|CHAOS, An Interdisciplinary Journal of Nonlinear Science|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam, The Netherlands|
Bisewski, K.L, Crommelin, D.T, & Mandjes, M.R.H. (2019). Rare event simulation for steady-state probabilities via recurrency cycles. CHAOS, An Interdisciplinary Journal of Nonlinear Science, 29(3). doi:10.1063/1.5080296