In the past, designing probability samples for biological field studies has been limited by the difficulty of locating random points in the field. The ability to receive precise GPS signals in many field settings has largely removed this constraint. We have been investigating statistical and computer-based technologies that enable field data collectors to use more sophisticated probability samp...

We present a probabilistic logic program to generate an educational puzzle that introduces the basic principles of next generation sequencing, gene finding and the translation of genes to proteins following the central dogma in biology. In the puzzle, a secret ”protein word” must be found by assembling DNA from fragments (reads), locating a gene in this sequence and translating the gene to a pr...

Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider a two-stage cluster sampling design where the clusters are first selected with probability proportional to cluster size, and then units are randomly sampled ...

This paper proposes a fully connected neural network model to map samples from a uniform distribution to samples of any explicitly known probability density function. During the training, the Jensen-Shannon divergence between the distribution of the model’s output and the target distribution is minimized. We experimentally demonstrate that our model converges towards the desired state. It provi...

The sample distribution is defined as the distribution of the sample measurements given the selected sample. Under informative sampling, this distribution is different from the corresponding population distribution, although for several examples the two distributions are shown to be in the same family and only differ in some or all the parameters. A general approach of approximating the margina...

The kernel embedding of distributions is a popular machine learning technique to manipulate probability distributions and is an integral part of numerous applications. Its empirical counterpart is an estimate from a finite set of samples from the distribution under consideration. However, for large-scale learning problems the empirical kernel embedding becomes infeasible to compute and approxim...

A fundamental result by Karger [10] states that for any λ-edgeconnected graph with n nodes, independently sampling each edge with probability p = Ω(logn/λ) results in a graph that has edge connectivity Ω(λp), with high probability. This paper proves the analogous result for vertex connectivity, when sampling vertices. We show that for any k-vertex-connected graph G with n nodes, if each node is...

Choice preferences can shift depending on whether outcome and probability information about the options are provided in a description or learned from the experience of sampling. We explored whether this description-experience “gap” could be explained as a difference in probabilistic mindset, that is, the explicit consideration of probability information in the former but not the latter. We repl...

This paper aims to estimate the joint default probability under the structuralform model with a random environment; namely stochastic correlation. By means of a singular perturbation method, we obtain an asymptotic expansion of a two-name joint default probability under a fast mean-reverting stochastic correlation model. The leading order term in the expansion is a joint default probability wit...

Principles of probability survey design were applied to guide large-scale sampling of populations of stony corals and associated benthic taxa in the Florida Keys coral reef ecosystem. The survey employed a two-stage stratified random sampling design that partitioned the 251-km(2) domain by reef habitat types, geographic regions, and management zones. Estimates of the coefficient of variation (r...