TY - GEN
T1 - Bayesian robustness in the control of gene regulatory networks
AU - Pal, Ranadip
AU - Datta, Aniruddha
AU - Dougherty, Edward R.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Bayesian
KW - Control of genetic regulatory networks
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=47849130581&partnerID=8YFLogxK
U2 - 10.1109/SSP.2007.4301212
DO - 10.1109/SSP.2007.4301212
M3 - Conference contribution
AN - SCOPUS:47849130581
SN - 142441198X
SN - 9781424411986
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
SP - 31
EP - 35
BT - 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
T2 - 2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
Y2 - 26 August 2007 through 29 August 2007
ER -