Stochastic processes are widely used for model building in the social, physical, engineering
and life sciences as well as in financial economics. In model building, statistical inference for …

Stochastic Filtering Theory uses probability tools to estimate unobservable stochastic
processes that arise in many applied fields including communication, target-tracking, and …

The paper studies the filtering problem for a non-classical frame-work: we assume that the
observation equation is driven by a signal dependent noise. We show that the support of the …

We establish large deviation estimates for the optimal filter where the observation process is
corrupted by a fractional Brownian motion. The observation process is transformed to an …

In this paper, we consider the nonlinear filtering problem for the signal process corrupted by
fractional Brownian motion noise. The signal process is assumed to be a Markov diffusion …

Multiple stochastic fractional integral expansions are applied to the problem of non-linear
filtering of a signal observed in the presence of an additive noise, where the noise is …

We consider a filtering model where the noise is an Ornstein-Uhlenbeck process
independent of the signal X. The signal is assumed to be a Markov difusion process. We …

The problem of nonlinear filtering of a random field observed in the presence of a noise,
modeled by a persistent fractional Brownian sheet of Hurst index (H1, H2) with 0.5< H1, H2< …

This is a companion paper of Crisan et el [4]. In this article, we study a few classes of
solvable models of the stochastic filtering problems with Ornstein-Uhlenbeck noise: Firstly …

We consider a linear filtering model (with feedback) when the observation noise is an
Ornstein-Ulhenbeck (OU) process with parameter β. The coefficients appearing in the model …