Bayesian Model Averaging (BMA) is a new area in modern applied statistics that provides data analysts with an efficient tool for discovering promising models and obtaining esti-mates of their posterior probabilities via Markov chain Monte Carlo (MCMC). These probabilities can be further used as weights for model averaged predictions and estimates of the parameters of interest. As a result, variance components due to model selection are estimated and accounted for, contrary to the practice of conventional data analysis (such as, for example, stepwise model selection). In addition, variable activation probabilities can be obtained for each variable of interest. This dissertation is aimed at connecting BMA and various ramifications of the multivari-ate technique called Reduced-Rank Regression (RRR). In particular, we are concerned with Canonical Correspondence Analysis (CCA) in ecological applications where the data are represented by a site by species abundance matrix with site-specific covariates. Our goal is to incorporate the multivariate techniques, such as Redundancy Analysis and Ca-nonical Correspondence Analysis into the general machinery of BMA, taking into account such complicating phenomena as outliers and clustering of observations within a single data-analysis strategy. Traditional implementations of model averaging are concerned with selection of variables. We extend the methodology of BMA to selection of subgroups of observations and im-plement several approaches to cluster and outlier analysis in the context of the multivari-ate regression model. The proposed algorithm of cluster analysis can accommodate re-strictions on the resulting partition of observations when some of them form sub-clusters that have to be preserved when larger clusters are formed. / Ph. D.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21080号 / 工博第4444号 / 新制||工||1691(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 KIM Chul-Woo, 教授 杉浦 邦征, 教授 八木 知己 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This research explores the potential of improving the explainability of outliers using Bayesian Belief Networks as background knowledge. Outliers are deviations from the usual trends of data. Mining outliers may help discover potential anomalies and fraudulent activities. Meaningful outliers can be retrieved and analyzed by using domain knowledge. Domain knowledge (or background knowledge) is represented using probabilistic graphical models such as Bayesian belief networks. Bayesian networks are graph-based representation used to model and encode mutual relationships between entities. Due to their probabilistic graphical nature, Belief Networks are an ideal way to capture the sensitivity, causal inference, uncertainty and background knowledge in real world data sets. Bayesian Networks effectively present the causal relationships between different entities (nodes) using conditional probability. This probabilistic relationship shows the degree of belief between entities. A quantitative measure which computes changes in this degree of belief acts as a sensitivity measure . The first contribution of this research is enhancing the performance for measurement of sensitivity based on earlier research work, the Interestingness Filtering Engine Miner algorithm. The algorithm developed (IBOX - Interestingness based Bayesian outlier eXplainer) provides progressive improvement in the performance and sensitivity scoring of earlier works. Earlier approaches compute sensitivity by measuring divergence among conditional probability of training and test data, while using only couple of probabilistic interestingness measures such as Mutual information and Support to calculate belief sensitivity. With ingrained support from the literature as well as quantitative evidence, IBOX provides a framework to use multiple interestingness measures resulting in better performance and improved sensitivity analysis. The results provide improved performance, and therefore explainability of rare class entities. This research quantitatively validated probabilistic interestingness measures as an effective sensitivity analysis technique in rare class mining. This results in a novel, original, and progressive research contribution to the areas of probabilistic graphical models and outlier analysis.

Clinical observations of individual patients are the cornerstones for furthering our understanding of the human body, diseases, and therapeutics. Traditionally, clinical observations were communicated through publishing case reports and case series. The effort of identifying and investigating unusual clinical observations has always rested on the shoulders of busy clinicians. To date, there has been little effort dedicated to increasing the efficiency of identifying unique and uncommon patient observations that may lead to valuable discoveries. In this thesis, we propose and implement an augmented intelligence framework to identify potential novel clinical observations by combining machine analytics through outlier analysis with the judgment of subject-matter experts. Preeclampsia is a significant cause of maternal and perinatal mortality and morbidity, and advances in its management have been slow. Considering the complex etiological nature of preeclampsia, clinical observations are essential in advancing our understanding of the disease and therapeutic approaches. Thus, the objectives and studies in this thesis aim to answer the hypothesis that using outlier analysis in preeclampsia-related medical data would lead to identifying previously uninvestigated clinical cases with new clinical insight. This thesis combines three articles published or submitted for publication in peer-reviewed journals. The first article (published) is a systematic review examining the extent to which case reports and case series in preeclampsia have contributed new knowledge or discoveries. We report that under one-third of the identified case reports and case series presented new knowledge. In our second article (submitted for publication), we provide an overview of outlier analysis and introduce the framework of augmented intelligence using our proposed extreme misclassification contextual outlier analysis approach. Furthermore, we conduct a systematic review of obstetrics-related research that used outlier analysis to answer scientific questions. Our systematic review findings indicate that such use is in its infancy. In our third article (published), we implement the proposed augmented intelligence framework using two different outlier analysis methods on two independent datasets from separate studies in preeclampsia and hypertensive disorders of pregnancy. We identify several clinical observations as potential novelties, thus supporting the feasibility and applicability of outlier analysis to accelerate clinical discovery.

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