O objetivo da dissertação é explorar Redes Bayesianas como ferramenta para reduzir incertezas nos processos de Inteligência Competitiva. Nela, através da revisão de conceitos de Planejamento Estratégico, Tomada de Decisão, Inteligência Competitiva e da capacidade de inferência de Redes Bayesianas é proposta uma abordagem de utilização destas redes com este intuito. Para tanto um estudo de caso apresenta o passo a passo da implementação da abordagem proposta em um ambiente simulado de gestão. No estudo de caso, cada uma das etapas da modelagem de cenários é descrita em detalhes, salientando os cuidados necessários para esta modelagem. Com a modelagem finalizada, dois quase-experimentos foram conduzidos em ambientes simulados para avaliar a percepção e o desempenho dos tomadores de decisão que utilizaram Redes Bayesianas em relação aos tomadores de decisão que não a utilizaram. Os dados obtidos no primeiro quase-experimento não se mostraram confiáveis e no segundo quase-experimento não formaram uma amostra significativa do ponto de vista estatístico. Não obstante, foi possível apresentar contribuições através das observações e dados obtidos nestes quaseexperimentos conduzidos. Do ponto de vista processual, falhas na construção dos quaseexperimento e sugestões de melhoria foram apresentadas. Quanto à ferramenta modelada e construída com base em Redes Bayesianas, foi possível identificar percepções do usuário relativas ao seu uso e sugestões de como aprimorá-la. Quanto aos dados de desempenho obtido, foi possível analisar, no segundo quase-experimento, indícios, mesmo que não conclusivos, que justificam a proposição de novos estudos para aprofundamento. Com base na literatura e nos indícios obtidos é possível acreditar que Redes Bayesianas podem ser usadas na redução de incerteza nos processos de inteligência competitiva e de tomada de decisão. / The aim of this work is to explore Bayesian Networks as a tool to reduce uncertainties in the process of Competitive Intelligence. Here, by reviewing the concepts of Strategic Planning, Decision Making, Competitive Intelligence and the ability to infer of Bayesian Networks, it is proposed an approach for using these networks with this purpose. For this, a case study presents a step by step implementation of the proposed approach in a simulated management environment. In the case study, each step of the modeling scenarios is described in detail, emphasizing the care required for this modeling. With the modeling complete, two quasi-experiments were conducted in simulated environments to assess the perception and performance of decision makers who used Bayesian networks in comparison to the decision makers who have not used it. Data from the first quasi-experiment were not reliable and the second quasi-experiment did not form a representative sample from the statistical point of view. Nevertheless, it was possible to make contributions through the observations and data from these quasi-experiments conducted. From the standpoint of procedural, flaws in the construction of quasiexperiments and suggestions for improvement were presented. Regarding the tool modeled and constructed based on Bayesian Networks, it was possible to identify user perceptions regarding their use and suggestions for how to improve it. As for the performance data obtained, it was possible to examine in the second quasi-experiment, evidence, while not conclusive, that justify the new studies on the subject. Based on the literature and the evidence obtained, it is the possible that Bayesian Networks can be used for reducing uncertainty in the process of competitive intelligence and decisionmaking.

abstract: Fraud is defined as the utilization of deception for illegal gain by hiding the true nature of the activity. While organizations lose around $3.7 trillion in revenue due to financial crimes and fraud worldwide, they can affect all levels of society significantly. In this dissertation, I focus on credit card fraud in online transactions. Every online transaction comes with a fraud risk and it is the merchant's liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions mostly focus on fraud detection accuracy and ignore financial considerations. Also, the highly effective manual review process is overlooked. First, I propose Profit Optimizing Neural Risk Manager (PONRM), a selective classifier that (a) constitutes optimal collaboration between machine learning models and human expertise under industrial constraints, (b) is cost and profit sensitive. I suggest directions on how to characterize fraudulent behavior and assess the risk of a transaction. I show that my framework outperforms cost-sensitive and cost-insensitive baselines on three real-world merchant datasets. While PONRM is able to work with many supervised learners and obtain convincing results, utilizing probability outputs directly from the trained model itself can pose problems, especially in deep learning as softmax output is not a true uncertainty measure. This phenomenon, and the wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. Thus, I present a mixed-integer programming framework for selective classification called MIPSC, that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. I also extend this framework to cost-sensitive settings (MIPCSC) and focus on the critical real-world problem, online fraud management and show that my approach outperforms industry standard methods significantly for online fraud management in real-world settings. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

The shortcomings of the proportion above cut (PAC) statistic used so prominently in the educational landscape renders it a very problematic measure for making correct inferences with student test data. The limitations of PAC-based statistics are more pronounced with cross-test comparisons due to their dependency on cut-score locations. A better alternative is using mean-based statistics that can translate to parametric effect-size measures. However, these statistics as well can be problematic. When Gaussian assumptions are not met, reasonable transformations of a score scale produce non-monotonic outcomes. The present study develops a distribution-wide approach to summarize trend, gap, and gap trend (TGGT) measures. This approach counters the limitations of PAC-based measures and mean-based statistics in addition to addressing TGGT-related statistics in a manner more closely tied to both the data and questions regarding student achievement. This distribution-wide approach encompasses visual graphics such as percentile trend displays and probability-probability plots fashioned after Receiver Operating Characteristic (ROC) curve methodology. The latter is framed as the P-P plot framework that was proposed by Ho (2008) as a way to examine trends and gaps with more consideration given to questions of scale and policy decisions. The extension in this study involves three main components: (1) incorporating Bayesian inference, (2) using a multivariate structure for longitudinal data, and (3) accounting for measurement error at the individual level. The analysis is based on mathematical assessment data comprising Grade 3 to Grade 7 from a large Midwestern school district. Findings suggest that PP-based effect sizes provide a useful framework to measure aggregate test score change and achievement gaps. The distribution-wide perspective adds insight by examining both visually and numerically how trends and gaps are affected throughout the score distribution. Two notable findings using the PP-based effect sizes were (1) achievement gaps were very similar between the Focal and Audit test, and (2) trend measures were significantly larger for the Audit test. Additionally, measurement error corrections using the multivariate Bayesian CTT approach had effect sizes disattenuated from those based on observed scores. Also, the ordinal-based effect size statistics were generally larger than their parametric-based counterparts, and this disattenuation was practically equivalent to that seen by accounting for measurement error. Finally, the rank-based estimator of P(X>Y) via estimated true scores had smaller standard errors than for its parametric-based counterpart.

The increasing importance of data in the modern world has created a need for new mathematical techniques to analyze this data. We explore and develop the use of geometry—specifically differential geometry—as a means for such analysis, in two parts. First, we provide a general framework to discover patterns contained in time series data using a geometric framework of assigning distance, clustering, and then forecasting. Second, we attempt to define a Riemannian metric on the space containing the data in order to introduce a notion of distance intrinsic to the data, providing a novel way to probe the data for insight.

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing. An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and will become more critical under a nonstationary climate, as sediment yields are expected to increase in regions of the world that will experience increased frequency, persistence, and intensity of storm events. Practical tools are needed to predict sediment erosion, transport and deposition and to characterize sediment sources within a reasonable measure of uncertainty. Water resource scientists and engineers use multidimensional data sets of varying types and quality to answer management-related questions, and the temporal and spatial resolution of these data are growing exponentially with the advent of automated samplers and in situ sensors (i.e., “big data”). Data-driven statistics and classifiers have great utility for representing system complexity and can often be more readily implemented in an adaptive management context than process-based models. Parametric statistics are often of limited efficacy when applied to data of varying quality, mixed types (continuous, ordinal, nominal), censored or sparse data, or when model residuals do not conform to Gaussian distributions. Data-driven machine-learning algorithms and Bayesian statistics have advantages over Frequentist approaches for data reduction and visualization; they allow for non-normal distribution of residuals and greater robustness to outliers. This research applied machine-learning classifiers and Bayesian statistical techniques to multidimensional data sets to characterize sediment source and flux at basin, catchment, and reach scales. These data-driven tools enabled better understanding of: (1) basin-scale spatial variability in concentration-discharge patterns of instream suspended sediment and nutrients; (2) catchment-scale sourcing of suspended sediments; and (3) reach-scale sediment process domains. The developed tools have broad management application and provide insights into landscape drivers of channel dynamics and riverine solute and sediment export.

Exposure to fine particulate matter (PM2.5) in the ambient air is associated with various health effects. There is increasing evidence which implicates the central role played by specific chemical components such as heavy metals of PM2.5. Given the fact that humans are exposed to complex mixtures of environmental pollutants such as PM2.5, research efforts are intensifying to study the mixtures composition and the emission sources of ambient PM, and the exposure-related health effects. Factor analysis as well source apportionment models are statistical tools potentially useful for characterizing mixtures in PM2.5. However, classic factor analysis is designed to analyze samples of independent data. To handle (spatio-)temporally correlated PM2.5 data, a Bayesian approach is developed and using source apportionment, a latent factor is converted to a mixture by utilizing loadings to compute mixture coefficients. Additionally there have been intensified efforts in studying the metal composition and variation in ambient PM as well as its association with health outcomes. We use non parametric smoothing methods to study the spatio-temporal patterns and variation of common PM metals and their mixtures. Lastly the risk of low birth weight following exposure to metal mixtures during pregnancy is being investigated.

Statistical analyses and modeling have contributed greatly to our understanding of the pathogenesis of HIV-1 infection; they also provide guidance for the treatment of AIDS patients and evaluation of antiretroviral (ARV) therapies. Various statistical methods, nonlinear mixed-effects models in particular, have been applied to model the CD4 and viral load trajectories. A common assumption in these methods is all patients come from a homogeneous population following one mean trajectories. This assumption unfortunately obscures important characteristic difference between subgroups of patients whose response to treatment and whose disease trajectories are biologically different. It also may lack the robustness against population heterogeneity resulting misleading or biased inference. Finite mixture models, also known as latent class models, are commonly used to model nonpredetermined heterogeneity in a population; they provide an empirical representation of heterogeneity by grouping the population into a finite number of latent classes and modeling the population through a mixture distribution. For each latent class, a finite mixture model allows individuals in each class to vary around their own mean trajectory, instead of a common one shared by all classes. Furthermore, a mixture model has ability to cluster and estimate class membership probabilities at both population and individual levels. This important feature may help physicians to better understand a particular patient disease progression and refine the therapeutical strategy in advance. In this research, we developed mixture dynamic model and related Bayesian inferences via Markov chain Monte Carlo (MCMC). One real data set from HIV/AIDS clinical management and another from clinical trial were used to illustrate the proposed models and methods. This dissertation explored three topics. First, we modeled the CD4 trajectories using a finite mixture model with four distinct components of which the mean functions are designed based on Michaelis-Menten function. Relevant covariates both baseline and time-varying were considered and model comparison and selection were based on such-criteria as Deviance Information Criteria (DIC). Class membership model was allowed to depend on covariates for prediction. Second, we explored disease status prediction HIV/AIDS using the latent class membership model. Third, we modeled viral load trajectories using a finite mixture model with three components of which the mean functions are designed based on published HIV dynamic systems. Although this research is motivated by HIV/AIDS studies, the basic concepts and methods developed here have much broader applications in management of other chronic diseases; they can also be applied to dynamic systems in other fields. Implementation of our methods using the publicly- vailable WinBUGS package suggest that our approach can be made quite accessible to practicing statisticians and data analysts.

The main goal of this research is to estimate the model parameters and to detect multiple change points in the long memory parameter of Gaussian ARFIMA(p, d, q) processes. Our approach is Bayesian and inference is done on wavelet domain. Long memory processes have been widely used in many scientific fields such as economics, finance and computer science. Wavelets have a strong connection with these processes. The ability of wavelets to simultaneously localize a process in time and scale domain results in representing many dense variance-covariance matrices of the process in a sparse form. A wavelet-based Bayesian estimation procedure for the parameters of Gaussian ARFIMA(p, d, q) process is proposed. This entails calculating the exact variance-covariance matrix of given ARFIMA(p, d, q) process and transforming them into wavelet domains using two dimensional discrete wavelet transform (DWT2). Metropolis algorithm is used for sampling the model parameters from the posterior distributions. Simulations with different values of the parameters and of the sample size are performed. A real data application to the U.S. GNP data is also reported. Detection and estimation of multiple change points in the long memory parameter is also investigated. The reversible jump MCMC is used for posterior inference. Performances are evaluated on simulated data and on the Nile River dataset.

The first goal of the present work focuses on the need for different rationing methods of the The Global Change and Financial Transition (GFT) work- ing group at the Potsdam Institute for Climate Impact Research (PIK): I provide a toolbox which contains a variety of rationing methods to be ap- plied to micro-economic disequilibrium models of the lagom model family. This toolbox consists of well known rationing methods, and of rationing methods provided specifically for lagom. To ensure an easy application the toolbox is constructed in modular fashion. The second goal of the present work is to present a micro-economic labour market where heterogenous labour suppliers experience consecu- tive job opportunities and need to decide whether to apply for employ- ment. The labour suppliers are heterogenous with respect to their qualifi- cations and their beliefs about the application behaviour of their competi- tors. They learn simultaneously – in Bayesian fashion – about their individ- ual perceived probability to obtain employment conditional on application (PPE) by observing each others’ application behaviour over a cycle of job opportunities. / In vorliegender Arbeit beschäftige ich mich mit zwei Dingen. Zum einen entwickle ich eine Modellierungstoolbox, die verschiedene Rationierungs- methoden enthält. Diese Rationierungsmethoden sind entweder aus der Literatur bekannt, oder wurden speziell für die lagom Modellfamilie ent- wickelt. Zum anderen zeige ich, dass man mit Hilfe von Rationierungsmetho- den aus der Modellierungstoolbox einen fiktiven Arbeitsmarkt modellie- ren kann. Auf diesem agieren arbeitssuchende Agenten, die heterogen im Bezug auf ihre Qualifikation und ihre Vorstellungen über das Bewerbungs- verhalten ihrer Konkurrenten sind. Sie erfahren aufeinanderfolgende Job- angebote und beobachten das Bewerbungsverhalten ihrer Konkurrenten, um in Bayesianischer Weise über ihre individuelle Wahrscheinlichkeit eine Stelle zu erhalten zu lernen.

This thesis deals with plants from the flowering plant order Lamiales, and especially the two families Scrophulariaceae and Stilbaceae. Both families have their main geographical distribution in southern Africa. The thesis presents phylogenies of Scrophulariaceae s. lat. that can be used as a framework both for a future formal classification of the Scrophulariaceae and of allied taxa. A new circumscription of the tribe Manuleeae of Scrophulariaceae is presented including also genera earlier placed in the tribe Selagineae (sometimes recognised as a family of its own, Selaginaceae). Manuleeae now consists of the genera: Barthlottia, Chaenostoma, Chenopodiopsis, Dischisma, Glekia, Globulariopsis, Glumicalyx, Gosela, Hebenstretia, Jamesbrittenia, Limosella, Lyperia, Manulea, Manuleopsis, Melanospermum, Phyllopodium, Polycarena, Pseudoselago, Reyemeia, Selago, Strobilopsis, Sutera, Tetraselago, Trieenea and Zaluzianskya. The genera Sutera and Selago are given new circumscriptions; Sutera is divided into two genera, Sutera and Chaenostoma. Selago is circumscribed to contain also taxa that formerly have been placed in Microdon and Cromidon. A new circumscription and infrafamiliar classification of the family Stilbaceae is also presented. Stilbaceae will consist of the three tribes: Bowkerieae, consisting of the genera Anastrabe, Bowkeria and Ixianthes; Hallerieae, consisting of Charadrophila and Halleria; and Stilbeae, consisting of Nuxia and Stilbe. Furthermore, the genera Campylostachys, Euthystachys, Kogelbergia and Retzia are all included in the genus Stilbe. The results in the thesis are based on parsimony and Bayesian phylogenetic inferences of DNA sequence data. Further, morphological characters are analysed and compared to the molecular phylogenies.