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We develop computational bounds on performance for causal state feedback stochastic control with linear dynamics, arbitrary noise distribution, and arbitrary input constraint set. This can be very use...
This paper considers moving horizon estimation (MHE) approach to solution of staged quadratic programming (QP) problems. Using an insight into the constrained solution structure for the growing horizo...
We present an alternating augmented Lagrangian method for convex optimization problems where the cost function is the sum of two terms, one that is separable in the variable blocks, and a second that ...
In this paper, we develop mathematical machinery for verifying that a broad class of general state space Markov chains reacts smoothly to certain types of perturbations in the underlying transition st...
This paper is concerned with applying importance sampling as a variance reduction tool for computing extreme quantiles. A central limit theorem is derived for each of four proposed importance sampling...
In this chapter, we consider the question of how long the arrival process to the single-server queue needs to be observed in order to accurately estimate the long-run fraction of time that the workloa...
We study the estimation of steady-state performance measures from an Ad-valued stochastic process Y = {Y(t): t≥0} representing the output of a simulation. In many applications, we may be interested in...
We describe a new estimator of the stationary density of a Markov chain on general state space. The new estimator is easier to compute, converges faster, and empirically gives visually superior estima...
We introduce a family of generalized-method-of-moments estimators of the parameters of a continuous-time Markov process observed at random time intervals. The results include strong consistency, asymp...
In many stochastic models, it is known that the response surface corresponding to a particular performance measure is monotone in the underlying parameter. For example, the steady-state mean waiting t...
In many settings in which Monte Carlo methods are applied, there may be no known algorithm for exactly generating the random object for which an expectation is to be computed. Frequently, however, one...
We introduce a new class of Monte Carlo methods, which we call exact estimation algorithms. Such algorithms provide unbiased estimators for equilibrium expectations associated with real-valued functio...
Kalman [9] introduced a method for estimating the state of a discrete linear dynamic system subject to noise. His method is fast but has poor numerical properties. Duncan and Horn [3] showed that th...

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