Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 82-87).

abstract: This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We use Bayesian networks to specify prospective models of conditional dependence. Bayesian networks are determined between social psychological factors and conflict variables, and modeled by directed acyclic graphs, while the significant interactions are modeled as conditional probabilities. Since the data are sparse and multi-dimensional, we regress Gaussian mixture models (GMMs) against the data to estimate the conditional probabilities of interest. The parameters of GMMs are estimated using the expectation-maximization (EM) algorithm. However, the EM algorithm may suffer from over-fitting problem due to the high dimensionality and limited observations entailed in this data set. Therefore, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used for GMM order estimation. To assist intuitive understanding of the interactions of social variables and the intergroup conflicts, we introduce a color-based visualization scheme. In this scheme, the intensities of colors are proportional to the conditional probabilities observed. / Dissertation/Thesis / M.S. Electrical Engineering 2012

This thesis is concerned with the modelling of integer-valued time series. The data naturally occurs in various areas whenever a number of events are observed over time. The model considered in this study consists of a Gaussian copula with autoregressive-moving average (ARMA) dependence and discrete margins that can be specified, unspecified, with or without covariates. It can be interpreted as a 'digitised' ARMA model. An ARMA model is used for the latent process so that well-established methods in time series analysis can be used. Still the computation of the log-likelihood poses many problems because it is the sum of 2^N terms involving the Gaussian cumulative distribution function when N is the length of the time series. We consider an Monte Carlo Expectation-Maximisation (MCEM) algorithm for the maximum likelihood estimation of the model which works well for small to moderate N. Then an Approximate Bayesian Computation (ABC) method is developed to take advantage of the fact that data can be simulated easily from an ARMA model and digitised. A spectral comparison method is used in the rejection-acceptance step. This is shown to work well for large N. Finally we write the model in an R-vine copula representation and use a sequential algorithm for the computation of the log-likelihood. We evaluate the score and Hessian of the log-likelihood and give analytic solutions for the standard errors. The proposed methodologies are illustrated using simulation studies and highlight the advantages of incorporating classic ideas from time series analysis into modern methods of model fitting. For illustration we compare the three methods on US polio incidence data (Zeger, 1988) and we discuss their relative merits.

Master of Science / Department of Electrical and Computer Engineering / Christopher L. Lewis / This thesis presents a method to perform universal object segmentation on fused SICK laser range data and color CCD camera images collected from a mobile robot. This thesis also details the method of fusion. Fused data allows for higher resolution than range-only data and provides more information than color-only data. The segmentation method utilizes the Expectation Maximization (EM) algorithm to detect the location and number of universal objects modeled by a six-dimensional Gaussian distribution. This is achieved by continuously subdividing objects previously identified by EM. After several iterations, objects with similar traits are merged. The universal object model performs well in environments consisting of both man-made (walls, furniture, pavement) and natural objects (trees, bushes, grass). This makes it ideal for use in both indoor and outdoor environments. The algorithm does not require the number of objects to be known prior to calculation nor does it require a training set of data. Once the universal objects have been segmented, they can be processed and classified or left alone and used inside robotic navigation algorithms like SLAM.

The ability of Next-Generation Sequencing (NGS) to produce massive quantities of genomic data inexpensively has allowed to study the structure of viral populations from an infected host at an unprecedented resolution. As a result of a high rate of mutation and recombination events, an RNA virus exists as a heterogeneous "swarm". Virologists and computational epidemiologists are widely using NGS data to study viral populations. However, discerning rare variants is muddled by the presence of errors introduced by the sequencing technology. We develop and implement time- and cost-efficient strategy for NGS of multiple viral samples, and computational methods to analyze large quantities of NGS data and to handle sequencing errors. In particular, we present: (i) combinatorial pooling strategy for massive NGS of viral samples; (ii) kGEM and 2SNV — methods for viral population haplotyping; (iii) ShotMCF — a Multicommodity Flow (MCF) based method for frequency estimation of viral haplotypes; (iv) QUASIM — an agent-based simulator of viral evolution taking in account viral variants and immune response.

<p>The identification of the vocation of an unknown heavy-duty vehicle is valuable to parts manufacturers who may not have otherwise access to this information on a consistent basis. This study proposes a methodology for vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means and Expectation Maximization. These algorithms are used to first construct the operating profile of each vocation from a set of vehicles with known vocations. The vocation of an unknown vehicle is then determined using different assignment methods.</p> <p> </p> <p>These methods fall under two main categories: one-versus-all and one-versus-one. The one-versus-all approach compares an unknown vehicle to all potential vocations. The one-versus-one approach compares the unknown vehicle to two vocations at a time in a tournament fashion. Two types of tournaments are investigated: round-robin and bracket. The accuracy and efficiency of each of the methods is evaluated using the NREL FleetDNA dataset.</p> <p> </p> <p>The study revealed that some of the vocations may have unique operating profiles and are therefore easily distinguishable from others. Other vocations, however, can have confounding profiles. This indicates that different vocations may benefit from profiles with varying number of clusters. Determining the optimal number of clusters for each vocation can not only improve the assignment accuracy, but also enhance the computational efficiency of the application. The optimal number of clusters for each vocation is determined using both static and dynamic techniques. Static approaches refer to methods that are completed prior to training and may require multiple iterations. Dynamic techniques involve clusters being split or removed during training. The results show that the accuracy of dynamic techniques is comparable to that of static approaches while benefiting from a reduced computational time.</p>

Modeling financial time series is of great importance for being successful within the financial market. Hidden Markov Models is a great way to include the regime shifting nature of financial data. This thesis will focus on getting an in depth knowledge of Hidden Markov Models in general and specifically the parameter estimation of the models. The objective will be to evaluate if and how financial data can be fitted nicely with the model. The subject was requested by Nordea Markets with the purpose of gaining knowledge of HMM’s for an eventual implementation of the theory by their index development group. The research chiefly consists of evaluating the algorithmic behavior of estimating model parameters. HMM’s proved to be a good approach of modeling financial data, since much of the time series had properties that supported a regime shifting approach. The most important factor for an effective algorithm is the number of states, easily explained as the distinguishable clusters of values. The suggested algorithm of continuously modeling financial data is by doing an extensive monthly calculation of starting parameters that are used daily in a less time consuming usage of the EM-algorithm.

The work presented here is an important component of an on going project of developing an automated mass classification system for breast cancer screening and diagnosis for Digital Mammogram applications. Specifically, in this work the task of automatically separating mass tissue from normal breast tissue given a region of interest in a digitized mammogram is investigated. This is the crucial stage in developing a robust automated classification system because the classification depends on the accurate assessment of the tumor-normal tissue border as well as information gathered from the tumor area. In this work the Expectation Maximization (EM) method is developed and applied to high resolution digitized screen-film mammograms with the aim of segmenting normal tissue from mass tissue. Both the raw data and summary data generated by Laws' texture analysis are investigated. Since the ultimate goal is robust classification, the merits of the tissue segmentation are assessed by its impact on the overall classification performance. Based on the 300 image dataset consisting of 97 malignant and 203 benign cases, a 63% sensitivity and 89% specificity was achieved. Although, the segmentation requires further investigation, the development and related computer coding of the EM algorithm was successful. The method was developed to take in account the input feature correlation. This development allows other researchers at this facility to investigate various input features without having the intricate understanding of the EM approach.

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Indiana University-Purdue University Indianapolis (IUPUI) / The identification of the vocation of an unknown heavy-duty vehicle is valuable to parts manufacturers who may not have otherwise access to this information on a consistent basis. This study proposes a methodology for vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means and Expectation Maximization. These algorithms are used to first construct the operating profile of each vocation from a set of vehicles with known vocations. The vocation of an unknown vehicle is then determined using different assignment methods. These methods fall under two main categories: one-versus-all and one-versus-one. The one-versus-all approach compares an unknown vehicle to all potential vocations. The one-versus-one approach compares the unknown vehicle to two vocations at a time in a tournament fashion. Two types of tournaments are investigated: round-robin and bracket. The accuracy and efficiency of each of the methods is evaluated using the NREL FleetDNA dataset. The study revealed that some of the vocations may have unique operating profiles and are therefore easily distinguishable from others. Other vocations, however, can have confounding profiles. This indicates that different vocations may benefit from profiles with varying number of clusters. Determining the optimal number of clusters for each vocation can not only improve the assignment accuracy, but also enhance the computational efficiency of the application. The optimal number of clusters for each vocation is determined using both static and dynamic techniques. Static approaches refer to methods that are completed prior to training and may require multiple iterations. Dynamic techniques involve clusters being split or removed during training. The results show that the accuracy of dynamic techniques is comparable to that of static approaches while benefiting from a reduced computational time.