The need to calibrate increasingly complex statistical models requires a persistent effort for further advances on available, computationally intensive Monte-Carlo methods. We study here an advanced version of familiar Markov-chain Monte-Carlo (MCMC) algorithms that sample from target...

We consider Markov chains {[Gamma]n} with transitions of the form [Gamma]n=f(Xn,Yn)[Gamma]n-1+g(Xn,Yn), where {Xn} and {Yn} are two independent i.i.d. sequences. For two copies {[Gamma]n} and {[Gamma]n'} of such a chain, it is well known that provided E[log(f(Xn,Yn))]<0, where => is weak convergence. In...</0,>

A Bayesian perspective is taken to quantify the amount of information learned from observing a stochastic process, Xt, on the interval [0, T] which satisfies the stochastic differential equation, dXt = S([theta], t, Xt)dt+[sigma](t, Xt)dBt. Information is defined as a change in expected utility...