A predictive coding using markov chain
Pengfei Lu, Binjie Gu, Weihong Lu · 2005
Based on the relationship among the peak points and valley points of the probability density function (p.d.f.) of a stochastic process, whose p.d.f. may be multimodal, the drift coefficient of its associated diffusion process, the 'shift back to center' property of the Markov chain and the state transitive value of the chain, the paper introduces the algorithm for constructing the approximating model of the Markov chain of an Ito stochastic differential equation (AMMC). The results of simulations demonstrate that the variance of the prediction error of the AMMC is not only far smaller than that of the Burg lattice predictor, but also very close to constant. These properties of the algorithm are beneficial to predictor and predictive coding.