Classification by continuous-time observations in multiplicative noise II: numerical algorithm

This is the second part of the paper “Classification by continuous-time observations in multiplicative noise I: Formulae for Bayesian estimate” published in “Informatics and Applications,” 2017, 11(1). Investigations are aimed at estimation of a finite-state random vector given continuous-time noised observations. The key feature is that the observation noise intensity is a function of the estimated vector, which makes useless the known results in the optimal filtering. In the first part of the paper, the required estimate is obtained both in the explicit integral form and as a solution to a stochastic differential system with some jump processes in the right-hand side. The second part contains a numerical algorithm of the estimate approximate calculation together with its accuracy analysis. An example illustrating the performance of the proposed estimate is also presented.