Search results for "65C05"

showing 3 items of 13 documents

Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers

2018

We establish quantitative bounds for rates of convergence and asymptotic variances for iterated conditional sequential Monte Carlo (i-cSMC) Markov chains and associated particle Gibbs samplers. Our main findings are that the essential boundedness of potential functions associated with the i-cSMC algorithm provide necessary and sufficient conditions for the uniform ergodicity of the i-cSMC Markov chain, as well as quantitative bounds on its (uniformly geometric) rate of convergence. Furthermore, we show that the i-cSMC Markov chain cannot even be geometrically ergodic if this essential boundedness does not hold in many applications of interest. Our sufficiency and quantitative bounds rely on…

Statistics and ProbabilityMetropoliswithin-Gibbsgeometric ergodicity01 natural sciencesCombinatorics010104 statistics & probabilitysymbols.namesakeFOS: MathematicsMetropolis-within-GibbsApplied mathematicsErgodic theory0101 mathematicsGibbs measureQAMathematics65C40 (Primary) 60J05 65C05 (Secondary)Particle GibbsMarkov chainGeometric ergodicity010102 general mathematicsErgodicityuniform ergodicityProbability (math.PR)iterated conditional sequential Monte CarloMarkov chain Monte CarloIterated conditional sequential Monte CarloRate of convergencesymbolsUniform ergodicityparticle GibbsParticle filterMathematics - ProbabilityGibbs sampling
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Simulation of BSDEs with jumps by Wiener Chaos Expansion

2016

International audience; We present an algorithm to solve BSDEs with jumps based on Wiener Chaos Expansion and Picard's iterations. This paper extends the results given in Briand-Labart (2014) to the case of BSDEs with jumps. We get a forward scheme where the conditional expectations are easily computed thanks to chaos decomposition formulas. Concerning the error, we derive explicit bounds with respect to the number of chaos, the discretization time step and the number of Monte Carlo simulations. We also present numerical experiments. We obtain very encouraging results in terms of speed and accuracy.

Statistics and ProbabilityWiener Chaos expansionDiscretizationMonte Carlo methodTime stepConditional expectation01 natural sciences010104 statistics & probabilitybackward stochastic differential equations with jumpsFOS: MathematicsApplied mathematics60H10 60J75 60H35 65C05 65G99 60H070101 mathematicsMathematicsPolynomial chaosApplied MathematicsNumerical analysis010102 general mathematicsMathematical analysista111Probability (math.PR)numerical methodCHAOS (operating system)[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Modeling and SimulationScheme (mathematics)Mathematics - Probability
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Exact simulation of diffusion first exit times: algorithm acceleration

2020

In order to describe or estimate different quantities related to a specific random variable, it is of prime interest to numerically generate such a variate. In specific situations, the exact generation of random variables might be either momentarily unavailable or too expensive in terms of computation time. It therefore needs to be replaced by an approximation procedure. As was previously the case, the ambitious exact simulation of exit times for diffusion processes was unreachable though it concerns many applications in different fields like mathematical finance, neuroscience or reliability. The usual way to describe exit times was to use discretization schemes, that are of course approxim…

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Probability (math.PR)primary 65C05 secondary:60G40 68W20 68T05 65C20 91A60 60J60diffusion processes[MATH] Mathematics [math]Exit timeExit time Brownian motion diffusion processes rejection sampling exact simulation multi-armed bandit randomized algorithm.randomized algorithm[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]exact simulationFOS: MathematicsBrownian motionmulti-armed banditMathematics - ProbabilityRejection sampling
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