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...

Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasing...

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...

We develop an efficient Monte-Carlo algorithm to sample an ensemble of stochastic transition paths between stable states. In our description, paths are represented by chains of states linked by Markovian transition probabilities. Rate constants and mechanisms characterizing the transition may be determined from the path ensemble. We have previously devised several algorithms for sampling the pa...

Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including cluster ensembles with missing values, as well as row-distributed or column-distributed cluster ensembles. Existing cluster ensemble algorithms are applicable only to...

To evaluate the performance of the empirical predictors presented in San Martino et al. (2005), we compute the cases where they are the best, “equivalent” to the best (tables 1) or poor (table2). We consider only the case of equal unknown within cluster variances. First, we consider the cases in which each of the proposed predictors has the best performance, i.e. we compute the percentage of ca...

Most medical datasets are not balanced in their class labels. Indeed in some cases it has been no ticed that the given class labels do not accurately represent characteristics of the data record. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without cons...