Embedded Estimation Sequential Bayes Parameter Inference for the Ricker Dynamical System

Bayes Parameter Estimation for the Bivariate

Parameter estimation for nonlinear dynamical adjustment models

A recursive generalized least squares algorithm and a filtering based least squares algorithm are developed for input nonlinear dynamical adjustment models with memoryless nonlinear blocks followed by linear dynamical blocks. The basic idea is to use the filtering technique and to replace the unknown terms in the information vectors with their estimates. The simulation results show the performa...

Parameter Estimation for Linear Dynamical Systems

Linear systems have been used extensively in engineering to model and control the behavior of dynamical systems. In this note, we present the Expectation Maximization (EM) algorithm for estimating the parameters of linear systems (Shumway and Sto er, 1982). We also point out the relationship between linear dynamical systems, factor analysis, and hidden Markov models. Introduction The goal of th...

Sequential parameter estimation for stochastic systems

The quality of the prediction of dynamical system evolution is determined by the accuracy to which initial conditions and forcing are known. Availability of future observations permits reducing the effects of errors in assessment the external model parameters by means of a filtering algorithm. Usually, uncertainties in specifying internal model parameters describing the inner system dynamics ar...

statistical inference via empirical bayes approach for stationary and dynamic contingency tables

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