Anthony Brockwell

Learn More
This work explores the application of a machine learning tool, CART modeling, to storage devices. We have developed approaches to predict a device's performance as a function of input workloads, requiring no knowledge of the device internals. Two uses of CART models are considered: one that predicts per-request response times (and then derives aggregate(More)
Sensor devices and embedded processors are becoming widespread, especially in measurement/monitoring applications. Their limited resources (CPU, memory and/or communication bandwidth, and power) pose some interesting challenges. We need concise, expressive models to represent the important features of the data and that lend themselves to efficient(More)
We introduce a new method to price American-style options on underlying investments governed by stochastic volatility models. The method combines a standard gridding approach to solving the associated dynamic programming problem, with a sequential Monte Carlo scheme to estimate required posterior distributions of the latent volatility process. The method(More)
We introduce a novel methodology for sampling from a sequence of probability distributions of increasing dimension and estimating their normalizing constants. These problems are usually addressed using Sequential Monte Carlo (SMC) methods. The alternative Sequentially Interacting Markov Chain Monte Carlo (SIMCMC) scheme proposed here works by generating(More)
We introduce a control law for a class of unknown nonlinear continuous-time systems in which full state measurements are available. We show that as long as a certain feedback gain parameter is sufficiently large, the closed-loop system is stable. Furthermore, the magnitude of the control is bounded in the limit. As a corollary to the main result, we show(More)
As biometric authentication systems become more prevalent, it is becoming increasingly important to evaluate their performance. This paper introduces a novel statistical method of performance evaluation for these systems. Given a database of authentication results from an existing system, the method uses a hierarchical random effects model, along with(More)
We introduce an adaptive moving horizon control scheme for nonlinear stochastic systems. The scheme uses the recently developed particle filter to track the hidden state, as well as to estimate unknown parameters. In addition, expected costs are approximated by Monte Carlo integration where necessary. Although computationally intensive, the scheme has wide(More)