We present new MCMC algorithms for computing the posterior distributions and expectations of the unknown variables in undirected graphical models with regular structure. For demonstration purposes, we focus on Markov Random Fields (MRFs). By partitioning the MRFs into non-overlapping trees, it is possible to compute the posterior distribution of a particular tree exactly by conditioning on the ...

INTRODUCTION: The choice of gradient sampling orientations for DT-MRI experiments has exercised many groups in the field. Of particular interest is the design of sampling schemes that will ensure that the error / variance in tensor estimates is independent of the relative orientation of the tensor to the reference frame established by the sampling vectors, which others have termed ‘statistical ...

For high volume data streams and large data warehouses, sampling is used for efficient approximate answers to aggregate queries over selected subsets. Mathematically, we are dealing with a set of weighted items and want to support queries to arbitrary subset sums. With unit weights, we can compute subset sizes which together with the previous sums provide the subset averages. The question addre...

A total of 24 commercial fields of cabbages and Brussels sprouts were sampled in a grid fashion with 20-25 equally spaced cells with four plants per cell. Using this data base of 80-100 plants, we conducted computer stimulations to compare the treatment decisions that would be made for the major insect pests using published sequential sampling programs and a newly developed variable-intensity s...