No description available.

Thesis (M.A.) in Mathematics--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaf 34).

Thesis (Ph. D.)--Oregon Health & Science University, Department of Science & Engineering, September 2008. / Includes bibliographical references (leaves 118 - 130).

No description available.

No description available.

Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

Classification using mixture distributions to model each class has not received too much attention in the literature. The most important attempts use normal distributions as com- ponents in these mixtures. Recently developed methods have allowed the use of these kinds of models as a flexible approach for density estimation. Most of the methods de- veloped so far use plug-in estimates for the parameters and assume that the number of components in the mixture is known. We obtain a predictive classifier for the classes by using Markov Chain Monte Carlo techniques which allow us to obtain a sampling chain for the parameters. This fully Bayesian approach to classification has the advantage that the number of components for each class is taken as another variable parameter and integrated out of the classification. To achieve this we use a birth-and-death/Gibbs sampler algorithm developed by Stephens (1997). We use five different datasets, two simulated ones to test the methods on a single class and three real datasets to test the methods for classification. We look at different models to de- fine which gives better flexibility in the modelling and an overall better classification. We look at different types of priors for the means and dispersion matrices of the components. Joint conjugate priors and an independent conjugate priors for the means and dispersion matrices for the components are used. We use a model with a common dispersion matrix for all the components and another one with a reparametrisation of these dispersion ma- trices into size, shape and orientation (Banfield and Raftery (1993)). We allow the sizes to differ while keeping a common shape and orientation for the dispersion matrices of the components in a class. We found that this type of modelling with independent conjugate priors for the means and dispersions while allowing the sizes of the dispersions to vary gave the best results for classification purposes as it allowed great flexibility and separation between the compo- nents of the classes.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (p. 114).

Approved for public release; distribution is unlimited. / The use of higher-order statistics provides insight into signals which is not always available at lower orders. Additionally, Gaussian-distributed signals have the interesting characteristic of disappearing at higher orders. Because so much of the noise and inter- ference environment is Gaussian-distributed, higher-order statistics thus offer the promise of an additional method of noise reduction and interference mitigation. As communica- tions signals become more and more complex, any additional ability to reduce the effects of noise and interference will have a profound impact on communications, surveillance, and intelligence systems. / US Department of Defense author (civilian).

by Su, Shi Chun = 宇宙微波背景各向異性的非高斯特性與旋轉宇宙的規範 / by 蘇士俊. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 78-83). / Abstracts in English and Chinese. / by Su, Shi Chun = Yu zhou wei bo bei jing ge xiang yi xing de fei Gaosi te xing yu xuan zhuan yu zhou de gui fan / by Su Shijun. / Chapter 1 --- Review of Cosmic Microwave Background Anisotropies --- p.1 / Chapter 1.1 --- Robertson-Walker metric --- p.1 / Chapter 1.2 --- Cosmological Perturbation --- p.4 / Chapter 1.2.1 --- Scalar-Vector-Tensor Decomposition --- p.6 / Chapter 1.2.2 --- Gauge Transformations --- p.8 / Chapter 1.2.3 --- Scalar Perturbation --- p.8 / Chapter 1.3 --- Sachs-Wolfe Effect --- p.9 / Chapter 1.4 --- Spectrum of CMB Anisotropies --- p.11 / Chapter 1.4.1 --- Rotation Transformation of Spherical Harmonics --- p.14 / Chapter 1.5 --- Contaminations of the CMBA --- p.16 / Chapter 1.5.1 --- The Internal Linear Combination Method --- p.17 / Chapter 2 --- Review of Models of Rotating Universe --- p.22 / Chapter 2.1 --- Godel's Model of a Rotating Universe --- p.23 / Chapter 2.2 --- Bianchi Models of a Rotating Universe --- p.24 / Chapter 2.3 --- Constraints on the Rotation of our Universe --- p.26 / Chapter 3 --- Study of Non-Gaussian Properties of the CMB Anisotropies --- p.31 / Chapter 3.1 --- Methodology --- p.32 / Chapter 3.2 --- Suspicious Anomalies against the IGH --- p.33 / Chapter 3.3 --- Verifications of the Suspicious Anomalies --- p.37 / Chapter 3.3.1 --- Different Cleaning Methods --- p.37 / Chapter 3.3.2 --- Effects of the Foreground Contaminations --- p.39 / Chapter 3.4 --- Further Study and Discussion --- p.52 / Chapter 3.5 --- Conclusions --- p.56 / Chapter 4 --- CMB Constraint on the Rotation of the Universe --- p.57 / Chapter 4.1 --- The Einstein Field Equations with Rotational Perturbations --- p.58 / Chapter 4.2 --- Analytic Solutions of the EFEs for the Rotating Universe --- p.63 / Chapter 4.3 --- The Sachs-Wolfe Effects up to Second-Order due to the Rotation --- p.65 / Chapter 4.4 --- Constraints on Our Model --- p.69 / Chapter 4.5 --- Discussion --- p.72 / Chapter 4.6 --- Conclusions --- p.75 / Chapter 5 --- Summary of the Thesis --- p.76 / Bibliography --- p.78