Bayesian robustness in the control of gene regulatory networks

Ranadip Pal, Aniruddha Datta, Edward R. Dougherty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The presence of noise and the availability of a limited number of samples prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in general are the min-max (worst case) approach and the Bayesian approach. The min-max control approach is at times conservative because it gives too much importance to the scenarios which hardly occur in practice. Consequently, in this paper, we focus on the Bayesian approach for the control of gene regulatory networks.

Original languageEnglish
Title of host publication2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages31-35
Number of pages5
DOIs
StatePublished - 2007
Event2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States
Duration: Aug 26 2007Aug 29 2007

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
CountryUnited States
CityMadison, WI
Period08/26/0708/29/07

Keywords

  • Bayesian
  • Control of genetic regulatory networks
  • Robustness

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