@conference {
	title = {Local Stochastic Models in Large Scale Stochastic Hamiltonian Systems},
	author = {Blankenship,  Gilmer L., Baras, John S.},
	year = {1982},
	month = {10/1982},
	abstract = {We consider the problem of isolating the dynamical behavior of a portion of a system imbedded in a large stochastic system. The large scale system is assumed to have a Hamiltonian structure. The method employed is the derivation of the appropriate master equations. Several examples illustrate the theory.},
	address = {Virginia Beach, Virginia},
	booktitle = {The IEEE International Symposium on Large Scale Systems},
	pages = {pp. 474-478}
}
@conference {
	title = {Accurate Evaluation of Stochastic Wiener Integrals with Applications to Scattering and Nonlinear Filtering Problems},
	author = {Hopkins, William E., Blankenship,  Gilmer L., Baras, John S.},
	year = {1982},
	month = {01/1982},
	abstract = {Using s one approximation formulas for stochastic Wiener function Space integrals , it is possible to approximate~the~conditional densities which arise in the nonlinear filtering of diffusion processes to within O(n), with n \&gt;= 1 arbitrary, by n-fold ordinary integrals. The latter have the simple form of a {\textquoteright}rectangular rule", but their accuracy is an order of magnitude better . Then {\textendash} fold integral. can be further decomposed into a recursion involving in one dimensional integrals. The sequence is recursive in the increments of the observation process~in the filtering problem . It is not, however , recursive in time. The one dimensional integrals are naturally treated by an m - step Gaussian quadrature which has an error proportiona1 to nm! / 2" (2 m) : ). (The proportionality constant can be estimated and optimized. ) The computation of these individual integrals can be reduced further by exploiting~certain inherent symmetries of the problem, and by doing some preliminary, {\textquoteright}off-line{\textquoteright} computing. The end result is a highly accurate, computationally efficient numerical algorithm for ~evaluating conditional densities for a substantial class of non linear filtering problems . By accepting light reductions~in accuracy , One can Obtain an algorithm (apparently) fast enough, when efficiently coded, for "Online" recursive filtering in real-time~},
	address = {Shanghai, Peoples Republic of China, Gordon and Breach, New York},
	booktitle = {Recent Developments in Control Theory and its Applications, Proceedings of IEEE--Chinese Association for Automation, Bilateral Meeting on Control Systems},
	pages = {pp. 54-68}
}
