KTH Matematik |

Seminarierummet 3721, Institutionen för
matematik, KTH, Lindstedts väg 25, plan 7.
Karta!
The first matrix gives rise to a Markov chain X(n), n=0,1,2,... say, the second gives rise to a sequence of observations Y(n), n=0,1,2,... say. The quantity one usually is interested in is the conditional distribution of X(n) given the observations Y(m), m=0,1,2,...,n up to time n. The sequence of conditional distributions is itself a stochastic process since the observations are stochastic quantities and the sequence is often called the filtering process. It turns out that the filtering process is also a Markov chain with values in the set of probability vectors on the state space, a set which is a non-locally compact set if the state space is a denumerable set and the topology is determined in the natural way by the total variation. The purpose of my talk is to present some results regarding the convergence in distribution of the filtering process. |

Sidansvarig: Filip Lindskog Uppdaterad: 25/02-2009 |