This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linear subspace models or Dynamic Point Distribution Models (DPDMs), which can deal with both continuous and discontinuous appearance changes; the representation is learned fully automatically from training data. The repre...

In this paper we study continuous ow nite buuer systems with input rates modulated by Markov chains. Discrete event simulations are applied for estimating loss probabilities. The simulations are executed under a twisted version of the original probability measure (importance sampling). We present a simple rule for determining a new measure, then show that the new measure matches thèmost likely'...

In traditional automated manufacturing, the motions of an industry robot are programmed step by step by humans for a specific task, which is costly and timeconsuming. This greatly limits the application of industry robot when the production is relatively small. This thesis proposes two touch-based localization approaches extended from the importance-sampling particle filter for a potentially la...

Let (Xn : n ≥ 0) be a sequence of iid rv’s with mean zero and finite variance. We present an efficient statedependent importance sampling algorithm for estimating the tail of Sn = X1 + ...+Xn in a large deviations framework as n ↗ ∞. Our algorithm can be shown to be strongly efficient basically throughout the whole large deviations region as n ↗ ∞ (in particular, for probabilities of the form P...

An importance sampling (IS) simulation method is presented for evaluating the lower-bound symbol error rate (SER) of the Bayesian decision feedback equalizer (DFE) with -PAM symbols, under the assumption of correct decision feedback. By exploiting an asymptotic property of the Bayesian DFE, a design procedure is developed, which chooses appropriate bias vectors for the simulation density to ens...

In recent years, particle filters have emerged as a useful tool that enables the application of Bayesian reasoning to problems requiring dynamic state estimation. The efficiency and accuracy of these type of filters are highly dependent on an appropriate propagation of the particles in time. In this paper we present a new method to improve the propagation step of the regular particle filter. Us...

We consider situations in Bayesian analysis where we have a family of priors νh on the parameter θ, where h varies continuously over a space H, and we deal with two related problems. The first involves sensitivity analysis and is stated as follows. Suppose we fix a function f of θ. How do we efficiently estimate the posterior expectation of f(θ) simultaneously for all h in H? The second problem...

The focus of this paper is on the sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences. An intron from the chimpanzee α-fetoprotein gene, which plays an important role in embryonic development in mammals is analysed. Three main aims are considered : (i) to assess the sensitivity to prior specification in Bayesian hidden Markov m...

For a discrete-time finite-state Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This scheme updates the change of measure at every transition using constant or decreasing step-size stochastic approximation. The updates are shown to concentrate asymptotically in a neighborhood of the desired zero varian...

Abstract. Simulated annealing — moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions — has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. Here, it is shown how one can use the Markov chain transitions for such an annealing sequence to define an importance sampler. The Markov chain aspec...