This paper proposes a multiple object tracking system for spatially extended objects, whose number is a priori not known and dynamically changing over time. Compared to the expected size of the objects, a high resolution range measuring sensor is used within an implementation of the proposed system. For that, the Bayesian framework is rigorously utilized and implemented using a reversible jump ...

Consider observations Y , distributed according to a mixture of densities Y P k j=1 w j f(j j); where 0 w j 1, P w j = 1, k and j correspond to unknown parameters of the mixture. In the a Bayesian framework, it is not possible to perform a default statistical analysis of the mixture using non-proper priors, N , for the component parameters, since the posterior distribution of these do not exist...

A Bayesian approach to multivariate adaptive regression spline (MARS) ®tting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995). The generated sample of MARS models produced is shown to have good predictive power when averaged and allows easy...

Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research the authors study finite mixture specifications for modeling consumer heterogeneity when each regression coefficient has its own finite mixture, that is, an attribute finite mixture model. An important challenge of such an approach to modeling heterogeneit...

This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed techniq...

In this paper, we discuss the statistical inference on the unknown parameters and reliability function of type-II extreme value (EVII) distribution when the observed data are progressively type-II censored. By applying EM algorithm, we obtain maximum likelihood estimates (MLEs). We also suggest approximate maximum likelihood estimators (AMLEs), which have explicit expressions. We provide Bayes ...

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We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features. We identify a simple generative process that results in the same distribution over equivalence classes, whi...

Automatic tracking of tagged MR image sequences is done frame-by-frame. For each frame, a quadrilateral (quad) detector is run over the image to give a set of “potential quads”. A likelihood function is specified for the detection of potential quads from an image. Quads are picked from the set of potential quads to form a “quilt”. Quads are present where a grid structure is apparent in the imag...