Interacting Markov chain Monte Carlo methods for solving nonlinear measure-valued equations. (English) Zbl 1198.65024

Let \((S^{(l)},{\mathcal S}^{(l)})_{l\geq 0}\) be a sequence of measurable spaces, \({\mathcal P}(S^{(l)})\) the set of probability measures on \(S^{(l)}\), \(\pi^{(l)}\in{\mathcal P}(S^{(l)})\), \(l\geq 0\), where \(\pi^{(0)}\) is known. For \(l\geq 1\) consider the equations \(\pi^{(l)}= \Phi_l(\pi^{(l-1)})\) for some mappings \(\Phi_l:{\mathcal P}(S^{(l-1)})\to{\mathcal P}(S^{(l)})\). The authors develop a new class of interacting Markov chain Monte Carlo (i-MCMC) methods for a numerical approximation of the solution. At level \(l=0\) a Markov chain Monte Carlo (MCMC) algorithm is run to obtain a chain \(X^{(0)}= (X^{(0)}_n)_{n\geq 0}\) targeting \(\pi^{(0)}\). (The “time” index \(n\) corresponds to the number of iterations of the i-MCMC algorithm.) The occupation measure of the chain \(X^{(0)}\) at time \(n\) is used to design a second MCMC algorithm to generate \(X^{(1)}= (X^{(1)}_n)_{n\geq 0}\) at level \(1\) targeting \(\pi^{(1)}\): The elementary transition \(X^{(1)}_n\rightsquigarrow X^{(1)}_{n+1}\) of the chain \(X^{(1)}\) at “time” \(n\) depends on the occupation measure of \((X^{(0)}_0, X^{(0)}_1,\dots, X^{(0)}_n)\).
Similarly, the empirical measure of \(X^{(l-1)}\) at level \(l-1\) is used to “feed” an MCMC algorithm generating \(X^{(l)}\) targeting \(\pi^{(l)}\) at level \(l\). Denote \(\overline X^{(m)}_n:= (X^{(0)}_n,\dots, X^{(m)}_n)\), \(\overline\pi^{[m]}:= \pi^{(1)}\otimes\cdots\otimes \pi^{(m)}\), let \(\delta_{X,n,m}\), \(\delta_{\overline X,n,m}\) be the Dirac measures at \(X^{(m)}_n\), \(\overline X^{(m)}_n\), respectively, and set \(\eta^{[m]}_n:= (n+1)^{-1}\cdot \sum^n_{p=1} \delta_{X,p,m}\), \(\overline\eta^{[m]}_n:= (n+1)^{-1}\cdot \sum^n_{p=1} \delta_{\overline X,p,m}\).
The main results may then be written as follows: For every \(r\geq 1\), \(m\geq 1\), and any bounded measurable function \(f\) on \(S^{(0)}\times\cdots\times S^{(m)}\) we have \(\sup_{n\geq 1}\sqrt{n}{\mathbf E}(|\overline\eta^{[m]}_n(f)- \overline\pi^{[m]}(f)|^r)< \infty\) and, under additional regularity conditions, \[ \forall t\limsup_{n\to\infty}(1/n)\log{\mathbf P}(|\overline\eta^{[m]}_n(f)- \overline\pi^{[m]}(f)|> 1)< -t^2/2\overline\sigma^2_m \] with some positive constant \(\overline\sigma<\infty\).
Finally, for some \(\alpha\in(0,1]\) and any sequence of bounded measurable functions \(f_n|S^{(n)}\) with \(|f_n|\leq 1\), \(\sup_{k\geq 0}\,\sup_{n\geq 0} n^{\alpha/2}{\mathbf E}(|\eta^{(k)}_n(f_n)- \pi^{(k)}(f_n)|^r)< \infty\).


65C05 Monte Carlo methods
47H20 Semigroups of nonlinear operators
60G35 Signal detection and filtering (aspects of stochastic processes)
60J85 Applications of branching processes
62G09 Nonparametric statistical resampling methods
47D08 Schrödinger and Feynman-Kac semigroups
47G10 Integral operators
62L20 Stochastic approximation
65C40 Numerical analysis or methods applied to Markov chains
60J22 Computational methods in Markov chains
60H25 Random operators and equations (aspects of stochastic analysis)
47J25 Iterative procedures involving nonlinear operators
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