Learn More
Recent advances in Bayesian hypothesis testing have led to the development of uniformly most powerful Bayesian tests, which represent an objective, default class of Bayesian hypothesis tests that have the same rejection regions as classical significance tests. Based on the correspondence between these two classes of tests, it is possible to equate the size(More)
A model of object shape by nets of medial and boundary primitives is justified as richly capturing multiple aspects of shape and yet requiring representation space and image analysis work proportional to the number of primitives. Metrics are described that compute an object representation's prior probability of local geometry by reflecting variabilities in(More)
I describe a simple procedure for investigating the convergence properties of Markov Chain Monte Carlo sampling schemes. The procedure employs multiple runs from a sampler, using the same random deviates for each run. When the sample paths from all sequences converge, it is argued that approximate equilibrium conditions hold. The procedure also provides a(More)
BACKGROUND Individual differences in human cognitive abilities show consistently positive correlations across diverse domains, providing the basis for the trait of "general intelligence" (g). At present, little is known about the evolution of g, in part because most comparative studies focus on rodents or on differences across higher-level taxa. What is(More)
In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter(More)
A framework is proposed for the analysis of ordinal categorical data when ratings from several judges are available. Emphasis focuses on the tasks of estimating quantiles of individual items, regressing item quantiles on observed covariates, and comparing the performance of raters. The model is illustrated in the design and evaluation of an automated essay(More)
Robust localization and segmentation of normal anatomical objects in medical images require (1) methods for creating descriptive object models that adequately capture object shape and expected shape variation across a population, (2) methods for combining such shape models with unclassified image data, and (3) means for localizing and extracting(More)
DRAFT 1 Preliminaries In a previous paper (Johnson 96), convergence properties of Gibbs samplers and certain other Markov Chain Monte Carlo (MCMC) algorithms were studied through a simple modiication of the manner in which random deviates were drawn to update variables. In the modiied procedures, \comparable" random deviates were used to update multiple(More)
A Bayesian method is presented for simultaneously segmenting and reconstructing emission computed tomography (ECT) images and for incorporating high-resolution, anatomical information into those reconstructions. The anatomical information is often available from other imaging modalities such as computed tomography (CT) or magnetic resonance imaging (MRI).(More)