A rich and  exible class of random probability measures, which we call stick-breaking priors, can be constructed using a sequence of independent beta random variables. Examples of random measures that have this characterization include the Dirichlet process, its two-parameter extension, the two-parameter Poisson–Dirichlet process, Ž nite dimensional Dirichlet priors, and beta two-parameter pro...

For the detection of hidden objects by low-frequency electromagnetic imaging the Linear Sampling Method works remarkably well despite the fact that the rigorous mathematical justification is still incomplete. In this work, we give an explanation for this good performance by showing that in the low-frequency limit the measurement operator fulfills the assumptions for the fully justified variant ...

Purpose of This Publication Knowledge of soil phosphorus (P) levels is an essential component of nutrient management planning for crop production and for tools that assess the risk of P loss from agricultural fields. Historically, soil P testing has been used to estimate P availability for crops and is now being used directly or as a component of P indices to assess the risk of P loss from fiel...

Traditional methods of computing standardized mortality ratios (SMR) in mortality studies rely upon a number of conventional statistical propositions to estimate confidence intervals for obtained values. Those propositions include a common but arbitrary choice of the confidence level and the assumption that observed number of deaths in the test sample is a purely random quantity. The latter ass...

Simple Cartesian scans, which collect Fourier transform data on a uniformly-spaced grid in the frequency domain, are by far the most common in MRI. But non-Cartesian trajectories such as spirals and radial scans have become popular for their speed and for other benefits, like making motion-correction easier [12]. A major problem in such scans, however, is reconstructing from nonuniform data, wh...

Selective sampling, a part of the active learning method, reduces the cost of labeling supplementary training data by asking for the labels only of the most informative, unlabeled examples. This additional information added to an initial, randomly chosen training set is expected to improve the generalization performance of a learning machine. We investigate some methods for a selection of the m...

Until 2007, the best computer programs for playing the board game Go performed at the level of a weak amateur, while employing the same Minimax algorithm that had proven so successful in other games such as Chess and Checkers. Thanks to a revolutionary new sampling-based planning approach named Upper Confidence bounds applied to Trees (UCT), today's best Go programs play at a master level on fu...

Correction for 'Sparse sampling methods in multidimensional NMR' by Mehdi Mobli et al., Phys. Chem. Chem. Phys., 2012, 14, 10835-10843.

A common problem in statistics is to compute sample vectors from a multivariate Gaussian distribution with zero mean and a given covariance matrix A. A canonical approach to the problem is to compute vectors of the form y = Sz, where S is the Cholesky factor or square root of A, and z is a standard normal vector. When A is large, such an approach becomes computationally expensive. This paper co...

In this paper, we propose a stratified sampling algorithm in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum. These proportions converge to the optimal allocation in terms of variance reduction. And our stratified estimator is asymptotically normal with asymptotic variance e...