On a class of discrete generation interacting particle systems.
On a role of predictor in the filtering stability.
On adaptive control of a partially observed Markov chain
A control problem for a partially observable Markov chain depending on a parameter with long run average cost is studied. Using uniform ergodicity arguments it is shown that, for values of the parameter varying in a compact set, it is possible to consider only a finite number of nearly optimal controls based on the values of actually computable approximate filters. This leads to an algorithm that guarantees nearly selfoptimizing properties without identifiability conditions. The algorithm is based...
On European option pricing under partial information
We consider a European option pricing problem under a partial information market, i.e., only the security's price can be observed, the rate of return and the noise source in the market cannot be observed. To make the problem tractable, we focus on gap option which is a generalized form of the classical European option. By using the stochastic analysis and filtering technique, we derive a Black-Scholes formula for gap option pricing with dividends under partial information. Finally, we apply filtering...
On filtering over Îto-Volterra observations.
On improving sensitivity of the Kalman filter
The impact of additive outliers on a performance of the Kalman filter is discussed and less outlier-sensitive modification of the Kalman filter is proposed. The improved filter is then used to obtain an improved smoothing algorithm and an improved state-space model parameters estimation.
On recursive filters inverse to finite sequences in the minimum mean square sense
On some nonconventional problem of a state filtration
On the convergence of the ensemble Kalman filter
Convergence of the ensemble Kalman filter in the limit for large ensembles to the Kalman filter is proved. In each step of the filter, convergence of the ensemble sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the ensemble members, and bounds on the ensemble then give convergence.
On the convergence of the wavelet-Galerkin method for nonlinear filtering
The aim of the paper is to examine the wavelet-Galerkin method for the solution of filtering equations. We use a wavelet biorthogonal basis with compact support for approximations of the solution. Then we compute the Zakai equation for our filtering problem and consider the implicit Euler scheme in time and the Galerkin scheme in space for the solution of the Zakai equation. We give theorems on convergence and its rate. The method is numerically much more efficient than the classical Galerkin method....
On the derivation of the nonlinear discrete equations numerically integrating the Euler PDEs.
On the innovations conjecture of nonlinear filtering with dependent data.
On the optimality of a new class of 2D recursive filters
The purpose of this paper is to prove the minimum variance property of a new class of 2D, recursive, finite-dimensional filters. The filtering algorithms are derived from general basic assumptions underlying the stochastic modelling of an image as a 2D gaussian random field. An appealing feature of the proposed algorithms is that the image pixels are estimated one at a time; this makes it possible to save computation time and memory requirement with respect to the filtering procedures based on strip...
On the phase portrait of the fast filtering algorithms
On the Phase Portrait of the Fast Filtering Algorithms
Fast filtering algorithms arising from linear filtering and estimation are nonlinear dynamical systems whose initial values are the statistics of the observation process. In this paper, we give a fairly complete description of the phase portrait for such nonlinear dynamical systems, as well as a special type of naturally related matrix Riccati equation.
On the stability of interacting processes with applications to filtering and genetic algorithms
Optimal control for third degree polynomial systems.
Optimal filtering for bilinear system states and its application to terpolymerization process identification.
Optimal linear filtering of general multidimensional Gaussian processes and its application to Laplace transforms of quadratic functionals.
Optimal reconstruction of state vector in 2-D systems