Chemical reaction rate parameter estimation by MAP particle filter algorithm

A fast search algorithm for estimation of chemical reaction kinetics parameters is investigated. A novel parameter estimation technique using maximum a posteriori (MAP) particle filter is presented. Low discrepancy sequences (LDS) are applied to generate a population of particles that are regarded as a set of samples drawn from a uniformly distributed probability density in a high dimensional parameter space. The notion of simulated annealing (SA) is adopted to "rejuvenate" a system of particles around an MAP estimate that is obtained through evolution of such filter particles.