JOINT STATE AND PARAMETER ESTIMATION FOR BOOLEAN DYNAMICAL SYSTEMS Conference Paper

abstract

• In a recent publication, a novel state-space signal model was proposed for discrete-time Boolean dynamical systems. The optimal recursive MMSE estimator for this model is called the Boolean Kalman filter (BKF), and an efficient algorithm was presented for its exact computation. In the present paper, we consider the system identification problem, i.e., the problem of parameter estimation for the case where only incomplete knowledge about the system is available. To solve this problem, we propose the application of the BKF in the context of the well-known paradigm of joint estimation of state and parameters. The approach is illustrated via a network inference example. 2012 IEEE.

name of conference

• 2012 IEEE Statistical Signal Processing Workshop (SSP)

authors

• Braga Neto, Ulisses

published proceedings

• 2012 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP)

author list (cited authors)

• Braga-Neto, U.

citation count

• 4

complete list of authors

• Braga-Neto, Ulisses

publication date

• January 2012

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Boolean Dynamical Systems
• Boolean Network Inference
• Optimal State Estimation
• System Identification

Digital Object Identifier (DOI)

• 10.1109/ssp.2012.6319800

International Standard Book Number (ISBN) 13

• 9781467301831

start page

• 704

end page

• 707

volume

• 1

URL

• http://dx.doi.org/10.1109/ssp.2012.6319800