@conference {
	title = {Combined Estimation and Control of HMMs},
	author = {Frankpitt, Bernard., Baras, John S.},
	year = {1997},
	month = {12/1997},
	abstract = {The principal contribution of this paper is the presentation of the potential theoretical results that are needed for an application of stochastic approximation theory to the problem of demonstrating asymptomatic stability for combined estimation and control of a plant described by a hidden Markov model. We motivate the results by briefly describing a combined estimation and control problem. We show how the problem translates to the stochastic approximation framework. We also show how the Markov chain that underlies the stochastic approximation problem~can be decomposed into factors with discrete and continuous range. Finally, we use this decomposition to develop the results that are needed for an application of the ODE method to the stochastic control problem.},
	address = {San Diego, CA},
	booktitle = {1997 IEEE Conference on Decision and Control},
	pages = {pp. 2254-2259}
}
@conference {
	title = {Estimation of Hidden Markov Models for Partially Observed Risk Sensitive Control Problems},
	author = {Frankpitt, Bernard., Baras, John S.},
	year = {1997},
	month = {07/1997},
	abstract = {We look at the problem of estimation for partially observed, risk-sensitive control problems with finite state, input and output sets, and receding horizon. We describe architectures for risk-sensitive controllers, and estimation, and we state conditions under which both the estimated model converges to the true model, and the control policy will converge to the optimal risk sensitive policy.},
	address = {Cyprus, Greece},
	booktitle = {5th IEEE Mediterranean Conference on Control and Systems}
}
@article {
	title = {Estimation of Hidden Markov Models for Partially Observed Risk Sensitive Control Problems},
	author = {Frankpitt, Bernard., Baras, John S.},
	year = {1998},
	month = {01/1998},
	abstract = {This paper provides a summary of our recent work on the problem of combined estimation and control of systems described by finite state, hidden Markov models. We establish the stochastic framework for the problem, formulate a separated control policy with risk-sensitive cost functional, describe an estimation scheme for the parameters of the hidden Markov model that describes the plant and finally indicate how the combined estimation and control problem can be reformulated in a framework that permits an application of stochastic approximation techniques to the proof of asymptotic convergence of the estimator.},
	pages = {pp 739-746},
	volume = {Vol 34},
	number = {No. 6},
	journal = {Kybernetika}
}
