This paper presents a data-driven cluster sampling framework for parsing scene images into generic regions (such as the sky, mountain and water) and objects (such as cows, horses and cars). We adopt generative models for both generic regions and objects, thus their likelihood probabilities are comparable and are learned under a common information projection principle. The inference algorithm fo...

Cluster stability research is involved with the validity of clusters generated by a clustering algorithm. In other words, it answers whether generated clusters are true clusters or due to chance. Estimating true numbers of clusters is related to this problem, since often the cluster validity is based on this estimate. In the literature, there are a number of methods available for both purposes....

In many surveys, characteristic of interest is sparsely distributed but highly aggregated; in such situations the adaptive cluster sampling is very useful. Examples of such populations can be found in fisheries, mineral investigations (unevenly distributed ore concentrations), animal and plant populations (rare and endangered species), pollution concentrations and hot spot investigations, and e...

Markov chain Monte Carlo (MCMC) methods have been used in many fields (physics, chemistry, biology, and computer science) for simulation, inference, and optimization. The essence of these methods is to simulate a Markov chain whose state X follows a target probability X ∼ π(X). In many applications, π(X) is defined on a graph G whose vertices represent elements in the system and whose edges rep...

When disease incidence locations are observed in a region, there is often interest in studying whether there is clustering about landmarks representing possible centralized sources of the disease. In this article we study a Bayesian approach to the detection and estimation of such landmarks. Spatial point processes are used to specify both the observation process and the prior distribution of t...

For better inference of the population quantity of interest, ratio estimators are often recommended when certain auxiliary variables are available. Two types of ratio estimators, modified for adaptive cluster sampling via transformed population and initial intersection probability approaches, have been studied in Dryver and Chao (2007). Unfortunately, none of them are a function of a minimal su...

Cluster computation power provides a promising way to improve response time in large data warehouses. On the other hand, the use of sampling summaries on the cluster for approximate answering of OLAP queries provides a very flexible system that can provide response time guarantees. In this paper we explore the cluster computation paradigm for data warehouses and summaries. The use of cluster co...

FRANCIS A. ROESCH, JR. Adaptive cluster sampling is shown to be a viable alternative for sampling forests when there are rare characteristics of the forest trees which are of interest and occur on clustered trees. The ideas of recent work in Thompson (1990) have been extended to the case in which the initial sample is selected with unequal probabilities. An example is given in which the initial...