Virtual move Monte Carlo is a cluster algorithm which was originally developed for strongly attractive colloidal, molecular, or atomistic systems in order to both approximate the collective dynamics and avoid sampling of unphysical kinetic traps. In this paper, we present the algorithm in the form, which selects the moving cluster through a wider class of virtual states and which is applicable ...

Studies of populations such as drug users encounter difficulties because the members of the populations are rare, hidden, or hard to reach. Conventionally designed large-scale surveys detect relatively few members of the populations so that estimates of population characteristics have high uncertainty. Ethnographic studies, on the other hand, reach suitable numbers of individuals only through t...

Time-location sampling (TLS) is used to collect information from hard-to-reach populations by sampling persons at locations at which they may be found. Epidemiologic studies using TLS have often been analyzed ignoring both clustering within locations and the differential probabilities that persons are sampled. I propose a weighted analysis reflecting approximate differential sampling probabilit...

The distance-dependent Chinese Restaurant Process (dd-CRP) is a flexible class of distributions over partitions which was recently introduced by [1, 2]. In their description and experiments Blei and Frazier focus on the sequential setting such as clustering over time. Their Gibbs sampler, while general in nature, does not explicitly handle the case of non-sequential (also called spatial) cluste...

One of the first challenges in a natural disaster or humanitarian emergency is to obtain accurate estimates of affected populations (1). In the aftermath of rapid-onset disasters such as the recent tsunami disaster in south Asia, there is frequently an absence of adequate baseline data against which to measure the impact of the disaster. Available population data vary widely in quality, and the...

We examine cluster-series methods for the behavior of simple fluids under confinement, applying the Mayer-sampling Monte Carlo method to evaluate the necessary cluster integrals. We examine two series formulations, one based on the density of a bulk phase in equilibrium with the confined phase (effectively specifying the chemical potential), and another based on the density of the confined phas...

We present a novel condition, which we term the network nullspace property, which ensures accurate recovery of graph signals representing massive network-structured datasets from few signal values. The network nullspace property couples the cluster structure of the underlying network-structure with the geometry of the sampling set. Our results can be used to design efficient sampling strategies...