abstract: Pipeline infrastructure forms a vital aspect of the United States economy and standard of living. A majority of the current pipeline systems were installed in the early 1900’s and often lack a reliable database reporting the mechanical properties, and information about manufacturing and installation, thereby raising a concern for their safety and integrity. Testing for the aging pipe strength and toughness estimation without interrupting the transmission and operations thus becomes important. The state-of-the-art techniques tend to focus on the single modality deterministic estimation of pipe strength and do not account for inhomogeneity and uncertainties, many others appear to rely on destructive means. These gaps provide an impetus for novel methods to better characterize the pipe material properties. The focus of this study is the design of a Bayesian Network information fusion model for the prediction of accurate probabilistic pipe strength and consequently the maximum allowable operating pressure. A multimodal diagnosis is performed by assessing the mechanical property variation within the pipe in terms of material property measurements, such as microstructure, composition, hardness and other mechanical properties through experimental analysis, which are then integrated with the Bayesian network model that uses a Markov chain Monte Carlo (MCMC) algorithm. Prototype testing is carried out for model verification, validation and demonstration and data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. With a view of providing a holistic measure of material performance in service, the fatigue properties of the pipe steel are investigated. The variation in the fatigue crack growth rate (da/dN) along the direction of the pipe wall thickness is studied in relation to the microstructure and the material constants for the crack growth have been reported. A combination of imaging and composition analysis is incorporated to study the fracture surface of the fatigue specimen. Finally, some well-known statistical inference models are employed for prediction of manufacturing process parameters for steel pipelines. The adaptability of the small datasets for the accuracy of the prediction outcomes is discussed and the models are compared for their performance. / Dissertation/Thesis / Doctoral Dissertation Materials Science and Engineering 2018

Este trabalho apresenta três principais contribuições: i. a proposta de uma nova lógica de descrição probabilística; ii. um novo algoritmo de inferência em primeira ordem a ser utilizado em terminologias representadas nessa lógica; e iii. aplicações práticas em problemas reais. A lógica aqui proposta, crALC (credal ALC), adiciona inclusões probabilísticas na popular lógica ALC combinando as terminologias com condições de aciclicidade, de Markov, e adotando uma semântica baseada em interpretações. Como os métodos de inferência exata tradicionalmente apresentam problemas de escalabilidade devido à presença de quantificadores (restrições universal e existencial), apresentamos um algoritmo de loopy propagation em primeira-ordem que se comporta bem para terminologias com domínios não triviais. Uma série de testes foi feita com o algoritmo proposto em comparação com algoritmos tradicionais da literatura; os resultados apresentados mostram uma clara vantagem em relação aos outros algoritmos. São apresentadas ainda duas aplicações da lógica e do algoritmo para resolver problemas reais da área de robótica móvel. Embora os problemas tratados sejam relativamente simples, eles constituem a base de muitos outros problemas da área, sendo um passo importante na representação de conhecimento de agentes/robôs autônomos e no raciocínio sobre esse conhecimento. / This work presents two major contributions: i. a new probabilistic description logic; ii. a new algorithm for inference in terminologies expressed in this logic; iii. practical applications in real tasks. The proposed logic, referred to as crALC (credal ALC), adds probabilistic inclusions to the popular logic ALC, combining the usual acyclicity and Markov conditions, and adopting interpretation-based semantics. As exact inference does not seem scalable due to the presence of quantifiers (existential and universal), we present a first-order loopy propagation algorithm that behaves appropriately for non-trivial domain sizes. A series of tests were done comparing the performance of the proposed algorithm against traditional ones; the presented results are favorable to the first-order algorithm. Two applications in the field of mobile robotics are presented, using the new probabilistic logic and the inference algorithm. Though the problems can be considered simple, they constitute the basis for many other tasks in mobile robotics, being a important step in knowledge representation and in reasoning about it.

A subfamília Mydinae (Insecta, Diptera, Mydidae) ocorre somente nas Américas e é composta por 12 gêneros e 84 espécies, sendo a grande maioria das espécies de Mydidae pertencentes a essa subfamília. Mydinae é atualmente dividida em quatro tribos: Dolichogastrini, Mydini, Phylomydini e Messiasiini. A monofilia da subfamília, assim como de suas tribos e gêneros, ainda não havia sido testada por análises filogenéticas, o que justifica os objetivos deste trabalho, que são: 1)testar a monofilia da subfamília Mydinae; 2)verificar o relacionamento filogenético dos Mydinae com outras subfamílias de Mydidae; 3)testar a monofilia das tribos, subtribos e gêneros de Mydinae, assim como a monofilia dos grupos de espécies do gênero Mydas; 4)propor uma nova classificação para a subfamília, baseada nos resultados filogenéticos. A partir de dados da morfologia externa de adultos, e também de sequência de DNA do gene COI, dois métodos de análise foram empregados: análises de parcimônia, com pesagem igual dos caracteres, e análises probabilísticas bayesianas. Para cada um dos métodos, foram analisados os dados morfológicos e moleculares separadamente, e também em conjunto. A monofilia de Mydinae, conforme delimitada na classificação vigente, não é corroborada no presente trabalho, em nenhuma das análises. Nas duas análises com dados morfológicos, e na análise bayesiana com dados morfológicos e moleculares, foi recuperado um clado formado por todos os Mydinae (exceto Messiasia wilcoxi) + Paramydas (\'Apiophorinae\'). Dentre as tribos de Mydinae, não foi recuperada a monifilia de Messiassiini e Mydini. Já os gêneros Ceriomydas, Stratiomydas, Phyllomydas e Protomydas foram reconhecidos como mofiléticos. Já os gêneros Baliomydas, Gauromydas, Messiasia e Mydas, não formaram grupos monofiléticos em nenhuma das análises. Neste trabalho, puderam ser testadas as monofilias de quatro dos cinco grupos de espécies de Mydas: clavatus, fulvifrons, interruptus e xanthopterus, sendo o grupo hardyi monotípico. Apenas o grupo interruptus foi recuperado como monofilético, embora seja reconhecido aqui que os caracteres de coloração tradicionalmente utilizados para a separação dos grupos não foram utilizados. A subfamília Apiophorinae, com amostragem de quatro espécies, não foi recuperada como monofilética, com o gênero Eumydas agrupando-se aos Rhopaliinae. A classificação de Mydinae é aqui revisada, porém devido à incerteza razoável quanto ao relacionamento entre alguns grupos, alguns táxons da classificação tradicional foram mantidos, apesar de não serem monofiléticos / The Mydinae (Insecta, Diptera, Mydidae) occur only in the Americas and comprise 12 genera and 84 species, of which the vast majority of mydids occurring in Brazil belonging to this subfamily. Mydinae is currently divided into four tribes: Dolichogastrini, Messiasiini, Mydini and Phylomydini. The monophyly of the subfamily, as well as the monophyly of their tribes and genera, had not yet been tested by phylogenetic analysis. Concerning this fact, the objectives of this work are: 1) test the monophyly of the subfamily Mydinae, 2) check the phylogenetic relationship between Mydinae and other subfamilies of Mydidae, 3) test the monophyly of the tribes, subtribes and genera of Mydinae, as well as the monophyly of the species-groups of the genus Mydas; 4) propose a new classification of the subfamily based on phylogenetic results. The data from the external morphology of adults, and also DNA sequence of the COI gene, two methods of analysis were used: parsimony analysis with equal weighting of characters, and Bayesian probabilistic analysis. For each method, morphological and molecular data were analyzed separately and also in combination. The monophyly of Mydinae, as defined in the current classification, is not borne out in the present study. In both analyzes with morphological data, and Bayesian analysis with morphological and molecular data, a clade formed by all Mydinae (except Messiasia wilcoxi) + Paramydas (\'Apiophorinae\') was recovered. Among the tribes of Mydinae, the monophylies of Messiassiini and Mydini were not recovered. The genera Ceriomydas, Stratiomydas, Phyllomydas and Protomydas are recognized as natural groups. In the other hand, the genera Baliomydas, Gauromydas, Messiasia and Mydas did not form monophyletic groups in any of the conducted analyzes. Concerning the Mydas species-groups, only the interruptus group was recovered as monophyletic, although it is recognized here that color based characters traditionally used for separating the groups were not used in the present work. The subfamily Apiophorinae, with four species sampled, was not recovered as monophyletic, with genus Eumydas grouping to Rhopaliinae. The classification of Mydinae is reviewed here, but due to reasonable uncertainty as to the relationships between some groups, some taxa of the traditional classification were kept, although not recognized as monophyletic

Notwithstanding the challenges associated with different methods of peptide identification, other methods have been explored over the years. The complexity, size and computational challenges of peptide-based data sets calls for more intrusion into this sphere. By relying on the prior information about the average relative abundances of bond cleavages and the prior probability of any specific amino acid sequence, we refine an already developed Bayesian approach in identifying peptides. The likelihood function is improved by adding additional ions to the model and its size is driven by two overall goodness of fit measures. In the face of the complexities associated with our posterior density, a Markov chain Monte Carlo algorithm coupled with simulated annealing is used to simulate candidate choices from the posterior distribution of the peptide sequence, where the peptide with the largest posterior density is estimated as the true peptide.

abstract: Bayesian Additive Regression Trees (BART) is a non-parametric Bayesian model that often outperforms other popular predictive models in terms of out-of-sample error. This thesis studies a modified version of BART called Accelerated Bayesian Additive Regression Trees (XBART). The study consists of simulation and real data experiments comparing XBART to other leading algorithms, including BART. The results show that XBART maintains BART’s predictive power while reducing its computation time. The thesis also describes the development of a Python package implementing XBART. / Dissertation/Thesis / Masters Thesis Statistics 2019

Motivated by the paucity of high quality stillbirth surveillance data and the spatial analyses of such data, the current research sets out to quantitatively describe the pattern of stillbirth events that may lead to mechanistic hypotheses. We broaden the appeal of Bayesian Poisson point process modeling to quantify excess risk while accounting for unobserved heterogeneity. We consider a practical data analysis strategy when fitting the point process model and study the utility of parameterizing the intensity function governing the point process to include a maternal contextual effect to account for variation due to multiple stillbirth events experienced by the same mother in independent pregnancies. Simulation studies suggest that our practical data analysis strategy is reasonable and that there is a variance-bias trade-off associated with the use of a maternal contextual effect. The methodology is applied to the spatial distribution of stillbirth events in Iowa during the years 2005 through 2011 obtained using an active, statewide public health surveillance approach. Several localized areas of excess risk were identified and mapped based on model components that captured the nuanced and salient features of the data. A conditional formulation of the point process model is then considered, which has two main advantages: the ability to easily incorporate covariate information attached to both stillbirth and live birth, as well as obviate the need to estimate the background intensity. We assess the utility of the conditional approach in the presence of unobserved heterogeneity, compare two Bayesian estimation techniques, and extend the conditional formulation to adequately capture spatio-temporal effects. The motivating study comes from the Iowa Registry for Congenital and Inherited Disorders who has a committed interest in the surveillance and epidemiology of stillbirth in Iowa and whether the occurrence might be geographically linked.

Water quality and river/stream ecosystems are important for all living creatures. To protect human health, aquatic life and the surrounding ecosystem, a considerable amount of time and money has been spent on sampling and monitoring streams and rivers. Water quality monitoring and analysis can help researchers predict and learn from natural processes in the environment and determine human impacts on an ecosystem. Measurements such as temperature, pH, nitrogen concentration, algae and fish count collected along the network are all important factors in water quality analysis. The main purposes of the statistical analysis in this thesis are (1) to assess the relationship between the variable measured in the water (response variable) and other variables that describe either the locations on/along the stream network or certain characteristics at each location (explanatory variable), and (2) to assess the degree of similarity between the response variable values measured at different locations of the stream, i.e. spatial dependence structure. It is commonly accepted that measurements taken at two locations close to each other should have more similarity than locations far away. However, this is not always true for observations from stream networks. Observations from two sites that do not share water flow could be independent of each other even if they are very close in terms of stream distance, especially those observations taken on objects that move passively with the water flow. To model stream network data correctly, it is important to quantify the strength of association between observations from sites that do not share water.

Validation and verification in machine learning is an open problem which becomes increasingly important as its applications becomes more critical. Amongst the applications are autonomous vehicles and medical diagnostics. These systems all needs to be validated before being put into use or else the consequences might be fatal. This master’s thesis focuses on improving both learning and validating machine learning models in cases where data can either be generated or collected based on a chosen position. This can for example be taking and labeling photos at the position or running some simulation which generates data from the chosen positions. The approach is twofold. The first part concerns modeling the relationship between any fixed-size set of positions and some real valued performance measure. The second part involves calculating such a performance measure by estimating the performance over a region of positions. The result is two different algorithms, both variations of Bayesian optimization. The first algorithm models the relationship between a set of points and some performance measure while also optimizing the function and thus finding the set of points which yields the highest performance. The second algorithm uses Bayesian optimization to approximate the integral of performance over the region of interest. The resulting algorithms are validated in two different simulated environments. The resulting algorithms are applicable not only to machine learning but can also be used to optimize any function which takes a set of positions and returns a value, but are more suitable when the function is expensive to evaluate.

This work offers two strategies to raise the prediction accuracy of Vector Autoregressive (VAR) Models. The first strategy is to improve the Minnesota prior, which is frequently used for Bayesian VAR models. The improvement is achieved in two ways. First, the variance-covariance matrix of regression disturbances is treated as unknown and random to incorporate parameter uncertainty. Second, the prior variance-covariance matrix of regression coefficients is constructed as a function of the variance-covariance matrix of disturbances, in order to account for dependencies between different equations. Since different prior specifications unavoidably lead to different models, and forecasting capability of any such model is often limited, the second strategy is to build an optimal prediction pool of models by using the conventional log predictive score function. The effectiveness of the proposed strategies is examined for one-step-ahead, multi-4-step-ahead, and single-4-step-ahead predictions through two exercises. One exercise is predicting national output, inflation, and interest rate in the United States, and the other is predicting state tax revenue and personal income in Iowa. The empirical results indicate that a properly selected prior can improve the prediction performance of a BVAR model, and that a real-time optimal prediction pool can outperform a single best constituent model alone.

The objective of this study is to develop a new modified Bayesian Kriging (MBKG) surrogate modeling method that can be used to carry out confidence-based reliability-based design optimization (RBDO) for problems in which simulation analyses are inherently noisy and standard Kriging approaches fail. The formulation of the MBKG surrogate modeling method is presented, and the full conditional distributions of the unknown MBKG parameters are derived and coded into a Gibbs sampling algorithm. Using the coded Gibbs sampling algorithm, Markov chain Monte Carlo is used to fit the MBKG surrogate model. A sequential sampling method that uses the posterior credible sets for inserting new design of experiment (DoE) sample points is proposed. The sequential sampling method is developed in such a way that the new DoE sample points added will provide the maximum amount of information possible to the MBKG surrogate model, making it an efficient and effective way to reduce the number of DoE sample points needed. Therefore, it improves the posterior distribution of the probability of failure efficiently. Finally, a confidence-based RBDO method using the posterior distribution of the probability of failure is developed. The confidence-based RBDO method is developed so that the uncertainty of the MBKG surrogate model is included in the optimization process. A 2-D mathematical example was used to demonstrate fitting the MBKG surrogate model and the developed sequential sampling method that uses the posterior credible sets for inserting new DoE. A detailed study on how the posterior distribution of the probability of failure changes as new DoE are added using the developed sequential sampling method is presented. Confidence-based RBDO is carried out using the same 2-D mathematical example. Three different noise levels are used for the example to compare how the MBKG surrogate modeling method, the sequential sampling method, and the confidence-based RBDO method behave for different amounts of noise in the response. A comparison of the optimization results for the three different noise levels for the same 2-D mathematical example is presented. A 3-D multibody dynamics (MBD) engineering block-car example is presented. The example is used to demonstrate using the developed methods to carry out confidence-based RBDO for an engineering problem that contains noise in the response. The MBD simulations for this example were done using the commercially available MBD software package RecurDyn. Deterministic design optimization (DDO) was first done using the MBKG surrogate model to obtain the mean response values, which then were used with standard Kriging methods to obtain the sensitivity of the responses. Confidence-based RBDO was then carried out using the DDO solution as the initial design point.