We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying ExpectationMaximization (EM) on the underlying maximum likelihood problem to estimate true answers as well as worker quality. Unfortunately, EM ...

INTRODUCTION Estimation of covariance components by Restricted Maximum Likelihood (REML) fitting an animal model is widely carried out using a derivative-free (DF) algorithm to locate the maximum of the likelihood function. While such algorithms have proven robust and easy to use, they are generally slow to converge, often requiring many likelihood evaluations, in particular for multivariate an...

Abstrcwt-We have developed a new projector model speciflcally tailored for fast list-mode tomographic reconstructions in Positrón emission tomography (PET) scanners with parallel planar detectors. The model provides an accurate estimation of the probability distribution of coincidence events defined by pairs of scintillating crystals. This distribution is parameterized with 2D elliptical Gaussi...

A recent trend in motion-based segmentation has been to rely on statistical procedures derived from expectationmaximization (EM) principles. EM-based approaches have various attractives for segmentation, such as proceeding by taking non-greedy soft decisions with regards to the assignment of pixels to regions, or allowing the use of sophisticated priors capable of imposing spatial coherence on ...

The corpus callosum (CC) is a key brain structure and change in its size and shape is a focal point in the study of neurodegenerative diseases like multiple sclerosis (MS). A number of automatic methods have been proposed for CC segmentation in magnetic resonance images (MRIs) that can be broadly classified as intensity-based and template-based. Imaging artifacts and signal changes due to patho...

This paper describes the use of probabilistic graphical models, in particular Bayesian networks (BN), for the detection of two problem invasive alien plants, C. odorata and L. camara, from the recently-launched 8-band Worldview-2 satellite imagery acquired on 26 September 2010 over central Swaziland. The main objective of this work was to apply and evaluate the efficacy of the very high resolut...

Model based recognition systems are designed to recognize the occurrence and pose of a known object in an input image that could contain zero or more occurrences of the object. A multiresolution statistical estimation approach to object recognition and pose estimation has been developed which incorporates models for the effects of background clutter, occlusion, and sensor noise. The resulting f...

This work relates the framework of model-based clustering for spatial functional data where the data are surfaces. We first introduce a Bayesian spatial spline regression model with mixed-effects (BSSR) for modeling spatial function data. The BSSR model is based on Nodal basis functions for spatial regression and accommodates both common mean behavior for the data through a fixed-effects part, ...

Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today’s fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely on the collaboration of individual routers and servers to directly measure network traffic. Even though the ...

The outage probability in a network of cooperative unmanned airborne vehicles (UAVs) over generalized fading channels is studied analytically using finite mixture with expectation maximization technique. A relay-based topology with one ground control unit (GCU) is considered, where the cooperative UAVs can communicate with the GCU directly or through relay. The application the UAV assigned for,...