Estimation of unknown pesticide levels in experimental samples is an important aspect of many agricultural and environmental studies. Such measurements are often made utilizing a “standard” dose response curve. This methodology compares the biological response of a target organism at known dosages to the response of the same organism exposed to an unknown sample. These “bioassays” are typically...

Do people use a single strategy or sample from multiple strategies when estimating the conjunction and disjunction of two independent events? Here we address this tension directly by comparing individual level Bayesian simulations of multi and single strategy models using data from a frequency estimation experiment. Participants were shown two statements describing attributes and asked to estim...

Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states th::�t evolve over continnous time_ A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transi­ tion model is a function of its parents. We address the problem of learning parameters ...

Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficult task, and most prevalent learning methods treat parameter estimation as a regression problem. The ...

The demand made upon computational analysis of observed symbolic sequences has been increasing in the last decade. Here, the concept of entropy receives applications, and the generalizations according to Tsallis H (T) q and R enyi H (R) q provide whole-spectra of entropies characterized by an order q. An enduring practical problem lies in the estimation of these entropies from observed data. Th...

Two major goals of this paper were, first to examine the cross-cultural consistency of the factor structure of the Hedonic and Eudaimonic Motives for Activities (HEMA) scale, and second to illustrate the advantages of using Bayesian estimation for such an examination. Bayesian estimation allows for more flexibility in model specification by making it possible to replace exact zero constraints (...

JU-HYUN PARK: Bayesian Density Regression and Predictor-Dependent Clustering. (Under the direction of Dr. David Dunson.) Mixture models are widely used in many application areas, with finite mixtures of Gaussian distributions applied routinely in clustering and density estimation. With the increasing need for a flexible model for predictor-dependent clustering and conditional density estimation...