Splitting methods in Approximate Bayesian Computation for partially observed diffusion processes
Sprache des Vortragstitels:
UQ hybrid seminar
Sprache des Tagungstitel:
ApproximateBayesian Computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed as an established tool for modelling time dependent,real world phenomena with underlying random effects. When applying ABC to stochastic models, two major difficulties arise. First, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the stochasticprocess under the same parameter configuration result in different trajectories. Second, exact simulation schemes to generate trajectories from the stochastic model are rarely available, requiring the derivation of suitable numerical methods for the syntheticdata generation. In this talk we consider SDEs having an invariant density and apply measure-preserving splitting schemes for the synthetic data generation. We illustrate the results of the parameter estimation with the corresponding ABC algorithm with simulateddata.This talk is based on joined work with Massimiliano Tamborrino, University of Warwick, and Irene Tubikanec, Johannes Kepler University Linz.