Partially observable Markov decision processes (POMDPs) have been
successfully applied to various robot motion planning tasks under uncertainty.
However, most existing POMDP algo...
Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analy...
We propose an approach to lossy source coding, utilizing ideas from Gibbs sampling, simulated annealing, and Markov Chain Monte Carlo (MCMC). The idea is to sample a reconstructio...
Background: Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions ...
Ryan Abo, Jathine Wong, Alun Thomas, Nicola J. Cam...
Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum...
Rex A. Kerr, Thomas M. Bartol, Boris Kaminsky, Mar...