Kim, Straub, and Keyes introduced the statistical temperature molecular dynamics (STMD) algorithm to overcome broken ergodicity by sampling a non­-Boltzmann flat energy histogram as noted in Kim, Straub, and Keyes, Phys. Rev. Lett. 97: 050601 (2007). Canonical averages are calculated via reweighting to the desired temperature. While STMD is promising, its application has been almost entirely to simple or model systems. In this dissertation the implementation of STMD into the biosimulation package CHARMM is used to simulate the methionine enkephalin pentamer peptide with a methione terminal cap in a droplet of CHARMM TIP3P water molecules. Chain thermodynamics is analyzed from the novel perspective of the statistical temperature as a function of potential energy, $TS(U), automatically generated by STMD. Both the minimum in the slope of $TS(U), and the peak in the heat capacity as a function of temperature, calculated via reweighting, indicate a collapse transition at Tθ ≈ 253K. Distributions of dihedral angles are obtained as a function of temperature. Rotamer regions found in the literature are reproduced, along with unique regions not found previously, including with advanced algorithms, indicating the power of STMD enhanced sampling.

<p>The focus of this work is on faults, and the main issue is statistical analysis and stochastic modeling of faults and fault patterns in petroleum reservoirs. The thesis consists of Part I-V and Appendix A-C. The units can be read independently. Part III is written for a geophysical audience, and the topic of this part is fault and fracture size-frequency distributions. The remaining parts are written for a statistical audience, but can also be read by people with an interest in quantitative geology. The topic of Part I and II is statistical model choice for fault size distributions, with a samling algorithm for estimating Bayes factor. Part IV describes work on spatial modeling of fault geometry, and Part V is a short note on line partitioning. Part I, II and III constitute the main part of the thesis. The appendices are conference abstracts and papers based on Part I and IV.</p> / Paper III: reprinted with kind permission of the American Geophysical Union. An edited version of this paper was published by AGU. Copyright [2000] American Geophysical Union

The focus of this work is on faults, and the main issue is statistical analysis and stochastic modeling of faults and fault patterns in petroleum reservoirs. The thesis consists of Part I-V and Appendix A-C. The units can be read independently. Part III is written for a geophysical audience, and the topic of this part is fault and fracture size-frequency distributions. The remaining parts are written for a statistical audience, but can also be read by people with an interest in quantitative geology. The topic of Part I and II is statistical model choice for fault size distributions, with a samling algorithm for estimating Bayes factor. Part IV describes work on spatial modeling of fault geometry, and Part V is a short note on line partitioning. Part I, II and III constitute the main part of the thesis. The appendices are conference abstracts and papers based on Part I and IV. / Paper III: reprinted with kind permission of the American Geophysical Union. An edited version of this paper was published by AGU. Copyright [2000] American Geophysical Union

We study the approximate answers to OLAP queries on data warehouses. We consider the relative answers to OLAP queries on a schema, as distributions with the L1 distance and approximate the answers without storing the entire data warehouse. We first introduce three specific methods: the uniform sampling, the measure-based sampling and the statistical model. We introduce also an edit distance between data warehouses with edit operations adapted for data warehouses. Then, in the OLAP data exchange, we study how to sample each source and combine the samples to approximate any OLAP query. We next consider a streaming context, where a data warehouse is built by streams of different sources. We show a lower bound on the size of the memory necessary to approximate queries. In this case, we approximate OLAP queries with a finite memory. We describe also a method to discover the statistical dependencies, a new notion we introduce. We are looking for them based on the decision tree. We apply the method to two data warehouses. The first one simulates the data of sensors, which provide weather parameters over time and location from different sources. The second one is the collection of RSS from the web sites on Internet.

Nous étudions les réponses proches à des requêtes OLAP sur les entrepôts de données. Nous considérons les réponses relatives aux requêtes OLAP sur un schéma, comme les distributions avec la distance L1 et rapprocher les réponses sans stocker totalement l'entrepôt de données. Nous présentons d'abord trois méthodes spécifiques: l'échantillonnage uniforme, l'échantillonnage basé sur la mesure et le modèle statistique. Nous introduisons également une distance d'édition entre les entrepôts de données avec des opérations d'édition adaptées aux entrepôts de données. Puis, dans l'échange de données OLAP, nous étudions comment échantillonner chaque source et combiner les échantillons pour rapprocher toutes requêtes OLAP. Nous examinons ensuite un contexte streaming, où un entrepôt de données est construit par les flux de différentes sources. Nous montrons une borne inférieure de la taille de la mémoire nécessaire aux requêtes approximatives. Dans ce cas, nous avons les réponses pour les requêtes OLAP avec une mémoire finie. Nous décrivons également une méthode pour découvrir les dépendances statistique, une nouvelle notion que nous introduisons. Nous recherchons ces dépendances en basant sur l'arbre de décision. Nous appliquons la méthode à deux entrepôts de données. Le premier simule les données de capteurs, qui fournissent des paramètres météorologiques au fil du temps et de l'emplacement à partir de différentes sources. Le deuxième est la collecte de RSS à partir des sites web sur Internet. / We study the approximate answers to OLAP queries on data warehouses. We consider the relative answers to OLAP queries on a schema, as distributions with the L1 distance and approximate the answers without storing the entire data warehouse. We first introduce three specific methods: the uniform sampling, the measure-based sampling and the statistical model. We introduce also an edit distance between data warehouses with edit operations adapted for data warehouses. Then, in the OLAP data exchange, we study how to sample each source and combine the samples to approximate any OLAP query. We next consider a streaming context, where a data warehouse is built by streams of different sources. We show a lower bound on the size of the memory necessary to approximate queries. In this case, we approximate OLAP queries with a finite memory. We describe also a method to discover the statistical dependencies, a new notion we introduce. We are looking for them based on the decision tree. We apply the method to two data warehouses. The first one simulates the data of sensors, which provide weather parameters over time and location from different sources. The second one is the collection of RSS from the web sites on Internet.

Made available in DSpace on 2016-06-02T20:06:10Z (GMT). No. of bitstreams: 1 6083.pdf: 1151427 bytes, checksum: 24c39bb02ef8c214a3e10c3cc5bae9ef (MD5) Previous issue date: 2014-03-14 / Financiadora de Estudos e Projetos / In this work, we consider the estimation of the number of errors in a software from a closed population. The process of estimating the population size is based on the capture-recapture method which consists of examining the software, in parallel, by a number of reviewers. The probabilistic model adopted accommodates situations in which reviewers are independent and homogeneous (equally efficient), and each error is an element that is part of a disjoint partition in relation to its detection probability. We propose an iterative process to obtain maximum likelihood estimates in which the EM algorithm is used to the nuisance parameters estimation. The estimates of population parameters were also obtained under the Bayesian approach, in which Monte Carlo on Markov Chains (MCMC) simulations through Gibbs sampling algorithm with insertion of latent variables were used on the conditional posterior distributions. The two approaches were applied to simulated data and in two real data sets from the literature. / Neste trabalho, consideramos a estimação do número de erros em um software provenientes de uma população fechada. O processo de estimação do tamanho populacional é baseado no método de captura-recaptura, que consiste em examinar o software, em paralelo, por certo número de revisores. O modelo probabilístico adotado acomoda situações em que os revisores são independentes e homogêneos (igualmente eficientes) e que cada erro é um elemento que faz parte de uma partição disjunta quanto à sua probabilidade de detecção. Propomos um processo iterativo para obtenção das estimativas de máxima verossimilhança em que utilizamos o algoritmo EM na estimação dos parâmetros perturbadores. As estimativas dos parâmetros populacionais também foram obtidas sob o enfoque Bayesiano, onde utilizamos simulações de Monte Carlo em Cadeias de Markov (MCMC) através do algoritmo Gibbs sampling com a inserção de variáveis latentes nas distribuições condicionais a posteriori. As duas abordagens foram aplicadas em dados simulados e em dois conjuntos de dados reais da literatura.

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