Recent Results on Bayesian Cramer-Rao Bounds for Jump Markov Systems
Paper in proceeding, 2016

In this paper, recent results on the evaluation of the Bayesian Cramer-Rao bound for jump Markov systems are presented. In particular, previous work is extended to jump Markov systems where the discrete mode variable enters into both the process and measurement equation, as well as where it enters exclusively into the measurement equation. Recursive approximations are derived with finite memory requirements as well as algorithms for checking the validity of these approximations are established. The tightness of the bound and the validity of its approximation is investigated on a couple of examples.

Computational modeling

Bayes methods

Mathematical model

Markov processes


Cramer-Rao bounds



Computer Science

Approximation algorithms

Monte Carlo methods


C. Fritsche

U. Orguner

Lennart Svensson

Chalmers, Signals and Systems

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

F. Gustafsson

2016 19th International Conference on Information Fusion (Fusion)

978-0-9964-5274-8 (ISBN)

Driving Forces

Sustainable development

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

Signal Processing

Areas of Advance

Materials Science



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