We study simulated annealing algorithms to maximise a function [psi] on a subset of . In classical simulated annealing, given a current state [theta]n in stage n of the algorithm, the probability to accept a proposed state z at which [psi] is smaller, is exp(-[beta]n+1([psi](z)-[psi]([theta]n))...

Hidden Markov models (HMMs) have during the last decade become a widespread tool for modelling sequences of dependent random variables. In this paper we consider a recursive estimator for HMMs based on the m-dimensional distribution of the process and show that this estimator converges to the...

We study the asymptotic performance of approximate maximum likelihood estimators for state space models obtained via sequential Monte Carlo methods. The state space of the latent Markov chain and the parameter space are assumed to be compact. The approximate estimates are computed by, firstly,...

The topic of this paper is linear optimal prediction of hidden Markov models (HMMs) and innovations representations of HMMs. Our interest in these topics primarily arise from subspace estimation methods, which are intrinsically linked to such representations. For HMMs, derivation of innovations...

We consider algorithms for simulation of iterated Itô integrals with application to simulation of stochastic differential equations. The fact that the iterated Itô integralconditioned on Wi(tn+h)-Wi(tn) and Wj(tn+h)-Wj(tn), has an infinitely divisible distribution utilised for the simultaneous...