In this dissertation, we have proposed two new algorithms for statistical inference for models with intractable normalizing constants: the Monte Carlo Metropolis-Hastings algorithm and the Bayesian Stochastic Approximation Monte Carlo algorithm. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. At each iteration, it replaces the unknown normalizing constant ratio by a Monte Carlo estimate. Although the algorithm violates the detailed balance condition, it still converges, as shown in the paper, to the desired target distribution under mild conditions. The BSAMC algorithm works by simulating from a sequence of approximated distributions using the SAMC algorithm. A strong law of large numbers has been established for BSAMC estimators under mild conditions. One significant advantage of our algorithms over the auxiliary variable MCMC methods is that they avoid the requirement for perfect samples, and thus it can be applied to many models for which perfect sampling is not available or very expensive. In addition, although the normalizing constant approximation is also involved in BSAMC, BSAMC can perform very robustly to initial guesses of parameters due to the powerful ability of SAMC in sample space exploration. BSAMC has also provided a general framework for approximated Bayesian inference for the models for which the likelihood function is intractable: sampling from a sequence of approximated distributions with their average converging to the target distribution. With these two illustrated algorithms, we have demonstrated how the SAMCMC method can be applied to estimate the parameters of ERGMs, which is one of the typical examples of statistical models with intractable normalizing constants. We showed that the resulting estimate is consistent, asymptotically normal and asymptotically efficient. Compared to the MCMLE and SSA methods, a significant advantage of SAMCMC is that it overcomes the model degeneracy problem. The strength of SAMCMC comes from its varying truncation mechanism, which enables SAMCMC to avoid the model degeneracy problem through re-initialization. MCMLE and SSA do not possess the re-initialization mechanism, and tend to converge to a solution near the starting point, so they often fail for the models which suffer from the model degeneracy problem.

The aim of this study is to investigate young adults’ perspectives on the increased shootings in their neighborhoods and how their perspectives relate to the public debate. This study also aims to inquire what measures young adults consider works against the increased shootings. The study is based on seven semi-structured interviews with young adults aged between 20 – 30 years from Järvaområdet. The empirical data was analyzed with inductive thematic analysis and resulted in four main themes. To describe and understand some of the themes we used labeling theory and the concepts of stigma and normalization. The research shows that the experience of the increased shootings has been normalized in the public debate and between the residents in the neighborhood. One of the findings indicates that the preventive measures doesn’t seems to match the needs in the neighborhoods. The overall conclusion shows that the shooting in the neighborhoods affects all in different levels and thus shows the importance to lift the different perspectives for instance future studies.

Heart failure patients are responsible for managing fluctuations in symptoms between exacerbations by employing treatment adherence, active monitoring, and management strategies based on expert guidelines. Despite education, delayed help seeking persists among those in the need of acute medical intervention, as evidenced by high hospital admission and readmission rates. The purpose of this qualitative grounded theory study was to explore the decision making processes undertaken by heart failure, community-dwelling individuals as they experience symptom changes. Eighteen face-to-face interviews were conducted with participants who had heart failure and received self-management education from a home care agency in Southern Ontario, Canada. Data were analyzed using iterative steps of open, axial, selective coding, and qualitative software text queries. Three process themes were identified: perceiving symptoms, normalizing symptoms, and adapting to symptoms, with an overarching theme of control and absence of consultative behaviors. The central concept revealed in this study was normalizing symptoms in heart failure which included actions taken by participants to mitigate symptom fluctuations. Daily fluctuations were assimilated into normal life resulting in desensitization of symptom recognition and a loss of functional capacity. These findings can be used to inform system changes needed to strengthen consultative patient-health professional relationships required for effective self-management problem-solving. This study leads to positive social change by explaining how self-management is practiced from the patient's perspective, which can inform practice recommendations and future research.

Fotografování sluneční koróny patří mezi nejobtížnější úlohy astrofotografie a zároveň je jednou z klíčových metod pro studium koróny. Tato práce přináší ucelený souhrn metod pro pozorování sluneční koróny pomocí snímků. Práce obsahuje nutnou matematickou teorii, postup pro zpracování snímků a souhrn adaptivních filtrů pro vizualizaci koronálních struktur v digitálních obrazech. Dále přináší návrh nových metod určených především pro obrazy s vyšším obsahem šumu, než je běžné u obrazů bílé koróny pořízených během úplných zatmění Slunce, např. pro obrazy pořízené pomocí úzkopásmových filtrů. Fourier normalizing-radial-graded filter, který byl navržen v rámci této práce, je založen na aproximaci hodnot pixelů a jejich variability pomocí trigonometrických polynomů s využitím dalších vlastností obrazu.

The task of Phone recognition is a fundamental task in Speech recognition and often serves a critical role in bench-marking purposes. Researchers have used a variety of models used in the past to address this task, using both generative and discriminative learning approaches. Among them, generative approaches such as the use of Gaussian mixture model-based hidden Markov models are always favored because of their mathematical tractability. However, the use of generative models such as hidden Markov models and its hybrid varieties is no longer in fashion owing to a large inclination to discriminative learning approaches, which have been found to perform better. The only downside is that these approaches do not always ensure mathematical tractability or convergence guarantees as opposed to their generative counterparts. So, the research problem was to investigate whether there could be a process of augmenting the modeling capability of generative Models using a kind of neural network based architectures that could simultaneously prove mathematically tractable and expressive. Normalizing flows are a class of generative models that have been garnered a lot of attention recently in the field of density estimation and offer a method for exact likelihood computation and inference. In this project, a few varieties of Normalizing flow-based hidden Markov models were used for the task of Phone recognition on the TIMIT dataset. It was been found that these models and their mixture model varieties outperformed classical generative model varieties like Gaussian mixture models. A decision fusion approach using classical Gaussian and Normalizing flow-based mixtures showed competitive results compared to discriminative learning approaches. Further analysis based on classes of speech phones was carried out to compare the generative models used. Additionally, a study of the robustness of these algorithms to noisy speech conditions was also carried out. / Uppgiften för fonemigenkänning är en grundläggande uppgift i taligenkänning och tjänar ofta en kritisk roll i benchmarkingändamål. Forskare har använt en mängd olika modeller som använts tidigare för att hantera denna uppgift genom att använda både generativa och diskriminerande inlärningssätt. Bland dem är generativa tillvägagångssätt som användning av Gaussian-blandnings modellbaserade dolda Markov-modeller alltid föredragna på grund av deras matematiska spårbarhet. Men användningen av generativa modeller som dolda Markov-modeller och dess hybridvarianter är inte längre på mode på grund av en stor lutning till diskriminerande inlärningsmetoder, som har visat sig fungera bättre. Den enda nackdelen är att dessa tillvägagångssätt inte alltid säkerställer matematisk spårbarhet eller konvergensgarantier i motsats till deras generativa motsvarigheter. Således var forskningsproblemet att undersöka om det kan finnas en process för att förstärka modelleringsförmågan hos generativa modeller med hjälp av ett slags neurala nätverksbaserade arkitekturer som samtidigt kunde visa sig matematiskt spårbart och uttrycksfullt. Normaliseringsflöden är en klass generativa modeller som nyligen har fått mycket uppmärksamhet inom området för densitetsberäkning och erbjuder en metod för exakt sannolikhetsberäkning och slutsats. I detta projekt användes några få varianter av Normaliserande flödesbaserade dolda Markov-modeller för uppgiften att fonemigenkänna i TIMIT-datasatsen. Det visade sig att dessa modeller och deras blandningsmodellvarianter överträffade klassiska generativa modellvarianter som Gaussiska blandningsmodeller. Ett beslutssmältningsstrategi med klassiska Gaussiska och Normaliserande flödesbaserade blandningar visade konkurrenskraftiga resultat jämfört med diskriminerande inlärningsmetoder. Ytterligare analys baserat på klasser av talsignaler utfördes för att jämföra de generativa modellerna som användes. Dessutom genomfördes en studie av robustheten hos dessa algoritmer till bullriga talförhållanden.

<p>The purpose with this dissertation is to study the Intersex phenomenon in South Africa, meaning the interplay between the dual sex and gender norms in society. Hence, the treatment by some medical institutions and the view of some non-medical institutions upon this ‘treatment’, have been studied in relation to the Intersex infant’s human rights and citizenship rights. The thesis has moreover also investigated how young Intersex children are included/excluded and mentioned/not mentioned within South Africa’s legal system and within UN’s Convention on the Rights of the Child.</p><p>Furthermore, because Intersex children are viewed as ‘different’ on two accounts – their status as infants and born with an atypical congenital physical sexual differentiation, the thesis’ theoretical framework looks at the phenomenon from three perspectives – ‘the politics of difference’, human rights, and citizenship rights directed towards infants. The theoretical frameworks have been used to ask questions in relation to the empirical data, i.e. look at how the Intersex infants are ‘treated’ in relation to their status as ‘different’; and also in relation to the concept of being recognized, respected and allowed to partake in deciding whether to impose surgery or not. Moreover, what ‘treatment’ serves the best interest of the Intersex child? This has been done through semi structured interviews.</p><p>In conclusion, some of the dissertation’s most important features are that since the South African society, like many other societies, strongly live by the belief that there are only two sexes and genders, this implies that Intersex infants do not fit in and become walking pathologies who must be ‘fixed’ to become ‘normal’. Moreover, since most genital corrective surgeries are imposed without being medically or surgically necessary, and are generally imposed before the age of consent (18), the children concerned, are generally not asked for their opinion regarding the surgery. Lastly because early corrective surgery can have devastating life lasting consequences, this ultimately means that the child’s human rights and citizenship rights are of a concern. These conclusions do however not ignore the consequences one has to endure for the price of being ‘different’.</p>

Made available in DSpace on 2016-04-25T18:39:33Z (GMT). No. of bitstreams: 1 Inacio Mitsuo Uchida.pdf: 3081805 bytes, checksum: 0f34c3bd8af5c744a5e4813b6a675b1f (MD5) Previous issue date: 2010-10-21 / This study seeks to answer the following question problem: what makes independent auditing companies cancel the registration with the CVM? It seeks to present a contribution to understanding the possible causes of the cancellation of auditors registration, considering the quantitative data of auditors canceled, researching the environment of accounting and auditing in Brazil, and the work of independent auditors in major markets in the independent audit Brazil, in other words, business- Traded and Financial Institutions for the period 1998 to 2009. The research method used was the multiple case study, with a selection of three national companies to independent audit that cleared the record in the Securities and Exchange Commission. The objectives of the research are: collecting data for statistical analysis with the competent organs; review the literature on the activities of the auditor and the independent auditing firms; researching the legal and technical requirements issued by standard setters and supervisory institutions of the profession. Based on analysis of the results, it intends the aspiration of reaching the conclusion that the selected companies cancel the registration, why not have a cost/benefits, to meet the requirements of regulating agencies, standard setters and the risks inherent in the activity independent audit / Este estudo busca responder a seguinte questão problema: quais os motivos que levam as empresas de auditoria independente a cancelarem o registro na CVM? Busca apresentar uma contribuição para o entendimento das possíveis causas do cancelamento de registro de auditores, considerando os dados quantitativos de auditores cancelados, pesquisando os ambientes da contabilidade e da auditoria no Brasil, bem como os trabalhos de auditoria independente nos maiores mercados de auditoria independente no Brasil, ou seja, nas empresas de Capital Aberto e nas Instituições Financeiras, para o período de 1998 a 2009. O método de pesquisa adotado foi o Estudo de Caso Múltiplo, com a seleção de três empresas nacionais de auditoria independente que cancelaram o registro na Comissão de Valores Mobiliários. São objetivos da pesquisa: coletar dados estatísticos para análise junto aos órgãos competentes; revisar a literatura sobre as atividades do auditor e das empresas de auditoria independente; pesquisar as exigências legais e técnicas emanadas dos órgãos normatizadores e fiscalizadores da profissão. Com base na análise sobre os resultados obtidos tem-se a aspiração de chegar à conclusão que as empresas selecionadas cancelam o registro, porque não apresentam uma relação custos/benefícios, para o cumprimento das exigências dos órgãos fiscalizadores e normatizadores e aos riscos inerentes a atividade de auditoria independente

Atrair usuários para o transporte coletivo e manter os que já utilizam é essencial para fomentar cidades mais sustentáveis. Melhorar a qualidade do transporte urbano por ônibus e considerar a visão do usuário, portanto, torna-se relevante. O benchmarking é uma ferramenta reconhecida de gestão da qualidade que permite comparar sistemas, identificar referências de boas práticas e promover trocas de experiência. Neste contexto, aliar o processo de benchmarking com avaliações de satisfação dos usuários do transporte coletivo tem um grande potencial para promover uma gestão mais efetiva e focada nas necessidades e desejos dos usuários do transporte. A comparação da percepção dos usuários de diferentes sistemas, entretanto, possui diversos desafios devido à falta de padronização na coleta de dados, subjetividade e vieses socioculturais dos respondentes. Este trabalho apresenta três métodos que buscam superar estes desafios e permitir a realização de análises de benchmarking com dados de satisfação dos usuários de diferentes cidades A primeira análise consiste na normalização das notas de satisfação para reduzir vieses sociais e culturais. A segunda aplica a análise envoltória de dados para identificar sistemas de transporte eficientes na visão dos seus usuários. Por fim, a terceira análise consiste na aplicação de análise de clusters para identificar relações entre perfis de usuários e as respectivas avaliações de satisfação em diferentes cidades. Os métodos mostram-se adequados para comparação de sistemas, permitindo identificação de metas, prioridades, benchmarks e entendimento de particularidades dos diferentes públicos. As análises apresentam distintos graus de complexidade de aplicação e de obtenção dos dados. Cada um dos métodos proporciona uma visão distinta a partir dos dados disponíveis, que permite que se definam benchmarks e auxilie na definição de diretrizes de melhorias. / Attracting users to public transport and maintaining the ones that already use it is essential to fostering more sustainable cities. Therefore, improving the quality of bus transit systems and considering the users’ vision becomes relevant. Benchmarking is a recognized quality management tool that allows comparing systems, identifying references to good practices and promoting exchanges of experience. Aligning benchmarking process and users’ satisfaction of public transport have great potential to become the management more focused and effective to the needs and desires of the users. However, comparing the perception of users of different systems results in several challenges due the lack of standardization of data collection, subjectivity and socio cultural biases. This study proposes the application of three methods aiming to overcome these challenges and to allow benchmarking analysis with users’ satisfaction data of different cities. The first analysis consists in normalizing satisfaction scores to reduce social and cultural biases. The second one applies Data Envelopment Analysis (DEA) to identify efficient transport systems in users’ view. The third one consists in using clusters analysis to identify relations between users’ profiles and their respective satisfaction in different cities. The methods are adequate for comparing systems, allowing goals identification, priorities, benchmarks and understanding of different audiences’ particularities. The analyses present different degrees of application complexity and data collection. Each method provides a distinct view from available data, which allows defining benchmarks and assist in improvements guidelines.

This thesis contributes to the field of Gaussian Graphical Models by exploring either numerically or theoretically various topics of Bayesian Methods in Gaussian Graphical Models and by providing a number of interesting results, the further exploration of which would be promising, pointing to numerous future research directions. Gaussian Graphical Models are statistical methods for the investigation and representation of interdependencies between components of continuous random vectors. This thesis aims to investigate some issues related to the application of Bayesian methods for Gaussian Graphical Models. We adopt the popular $G$-Wishart conjugate prior $W_G(\delta,D)$ for the precision matrix. We propose an efficient sampling method for the $G$-Wishart distribution based on the Metropolis Hastings algorithm and show its validity through a number of numerical experiments. We show that this method can be easily used to estimate the Deviance Information Criterion, providing a computationally inexpensive approach for model selection. In addition, we look at the marginal likelihood of a graphical model given a set of data. This is proportional to the ratio of the posterior over the prior normalizing constant. We explore methods for the estimation of this ratio, focusing primarily on applying the Monte Carlo simulation method of path sampling. We also explore numerically the effect of the completion of the incomplete matrix $D^{\mathcal{V}}$, hyperparameter of the $G$-Wishart distribution, for the estimation of the normalizing constant. We also derive a series of exact and approximate expressions for the Bayes Factor between two graphs that differ by one edge. A new theoretical result regarding the limit of the normalizing constant multiplied by the hyperparameter $\delta$ is given and its implications to the validity of an improper prior and of the subsequent Bayes Factor are discussed.

This thesis contributes to the field of Gaussian Graphical Models by exploring either numerically or theoretically various topics of Bayesian Methods in Gaussian Graphical Models and by providing a number of interesting results, the further exploration of which would be promising, pointing to numerous future research directions. Gaussian Graphical Models are statistical methods for the investigation and representation of interdependencies between components of continuous random vectors. This thesis aims to investigate some issues related to the application of Bayesian methods for Gaussian Graphical Models. We adopt the popular $G$-Wishart conjugate prior $W_G(\delta,D)$ for the precision matrix. We propose an efficient sampling method for the $G$-Wishart distribution based on the Metropolis Hastings algorithm and show its validity through a number of numerical experiments. We show that this method can be easily used to estimate the Deviance Information Criterion, providing a computationally inexpensive approach for model selection. In addition, we look at the marginal likelihood of a graphical model given a set of data. This is proportional to the ratio of the posterior over the prior normalizing constant. We explore methods for the estimation of this ratio, focusing primarily on applying the Monte Carlo simulation method of path sampling. We also explore numerically the effect of the completion of the incomplete matrix $D^{\mathcal{V}}$, hyperparameter of the $G$-Wishart distribution, for the estimation of the normalizing constant. We also derive a series of exact and approximate expressions for the Bayes Factor between two graphs that differ by one edge. A new theoretical result regarding the limit of the normalizing constant multiplied by the hyperparameter $\delta$ is given and its implications to the validity of an improper prior and of the subsequent Bayes Factor are discussed.