Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method. Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text] / Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu.

在真實世界中，我們可以觀察到許多財務或經濟變數(股價、匯率、利率等)有時波動幅度非常微小，呈現相對穩定的狀態(Regime)；有時會由於政治因素或經濟環境的變動，突然一段期間呈現瘋狂震盪的狀態。針對這種現象，已有學者提出狀態轉換波動度模型(Regime Switching Volatility Model，簡稱RSV)來捕捉此一現象。 本篇論文選擇每年交易金額非常龐大的外匯選擇權市場，以RSV模型為基礎，採用馬可夫鏈蒙地卡羅法 ( Markov Chain Monte Carlo，簡稱MCMC ) 中的吉普斯抽樣(Gibbs Sampling)法來估計RSV模型的參數，依此預測外匯選擇權在RSV模型下的價格。我們再將此價格與Black and Scholes(BS)法及實際市場交易的價格資料作比較，最後並提出笑狀波幅與隱含波動度平面的結果。結果顯示經由RSV模型與MCMC演算法所計算出來的選擇權價格確實優於傳統的BS方法，且能有效解釋波動率期間結構 (Volatility Term Structure) 與笑狀波幅 (Volatility Smile) 的現象，確實反應且捕捉到了市場上選擇權價格所應具備的特色。

End Stage Renal Disease (ESRD) is the irreversible loss of kidney function, which can be due to various causes. Its treatment is one of the most costly chronic disease treatments. There are now approximately one million people worldwide living with ESRD and this number is predicted to increase in the future. The main reasons for the increasing incidence of ESRD worldwide are population ageing, the rapid increase of diabetes mellitus reaching epidemic proportions, and changes in age limits for treatment initiation. In Greece, during the period 2005-2009, 74% of the ESRD patients were on hemodialysis (HD), 7% on peritoneal dialysis (PD) and 19% were living with a functioning graft. The latter percentage brings Greece in the 26th place out of 36 countries in prevalence of functioning grafts worldwide. Cost-effectiveness analyses of these treatments have shown that RTx is overall the least expensive, followed by PD, while centre HD is the most expensive. Moreover, these treatments are also listed in the exact same order concerning the quality of life they provide to patients. The main reasons for the low RTx rate in Greece are the lack of organ donation, largely due to inadequate information, the inefficient organ distribution system, a high number of private HD centers not interested in RTx, as well as social factors. The objective of the present work was to implement a model for the projection of the ESRD patients’ number by 2020 in Greece and investigate the impact of different scenarios of an increase in RTx. In addition, a cost-effectiveness analysis of the increase in RTx was performed. The projection was performed based on a Markov chain model. The Markov models are distinguished by their simplicity and their ability to accurately represent many clinical problems. A deterministic Markov chain model was implemented in order to predict the future number of prevalent ESRD patients in Greece. Monte Carlo techniques were applied in order to add robustness to the model. Thus two models of prediction were implemented, a Markov chain and a Markov Chain Monte Carlo (MCMC) model. Age-specific data (<45, 45-65, >65 age groups) on incident and prevalent ESRD patients’ number for Greece, available from the European Renal Association – European Dialysis and Transplant Association reports for the period 1998-2009, were used for the implementation. The basic component of the Markov chain is the transition matrix defining the probability for the patient to move between the four states, i.e. HD, PD, RTx and death. An iterative error minimization technique was used in defining the transition probabilities of the Markov chain, based on the data from 1998 to 2006. Both Markov chain and MCMC models were successfully validated based on data for the period from 2007 to 2009. In each model the ESRD incident patients’ number in Greece was predicted in a different way. For the Markov chain model three incidence rate scenarios were applied: low, medium and high. Additionally, two different approaches were proposed for the increase in RTx, one for each model. In the Markov chain model, two scenarios of RTx increase were applied on the number of prevalent patients. The first one was based on the assumption that the average number of transplants performed in Greece during the period 2005-2009 will double by 2020. The second one assumed that Greece will reach by 2020 the transplantation rate of Norway in 2009, the highest transplantation rate reported during that year worldwide. In the MCMC model, the increase of RTx was accomplished by increasing annually by 1% the number of incident patients receiving RTx and reducing accordingly the number of patients performing HD. The Markov chain model projected an increase in the number of prevalent patients’ in Greece by 19.3%, 24.4% and 42.2% in 2020 compared to 2009, depending on the incidence scenario applied. Similarly, the MCMC model projected a 25.0% prevalence increase. In the Markov chain model, the results of the increase in RTx indicated that in 2020 there will be a 64.6% (first scenario) or a 107.2% (second scenario) increase in the number of RTx patients compared to 2009, resulting in total saving of €50.2 and €112.37 million, respectively, for the period 2010-2020. Finally, the increase in RTx accomplished in the MCMC model indicated a 57.9% increase of patients living with a transplanted kidney, resulting in total saving of €68.2 million. The results of both models suggest that performing more kidney transplantations instead of HD would reduce the treatment costs for the country’s healthcare system, while at the same time it would improve the quality of life for a significant number of ESRD patients. / Η Τελικού Σταδίου Χρόνια Νεφρική Ανεπάρκεια (ΤΣΧΝΑ) είναι η μη αναστρέψιμη απώλεια της νεφρικής λειτουργίας, η οποία μπορεί να οφείλεται σε διάφορα αίτια. Η θεραπεία της είναι μία από τις πιο δαπανηρές όσον αφορά τις χρόνιες παθήσεις. Σήμερα, υπολογίζεται ότι περίπου ένα εκατομμύριο άνθρωποι παγκοσμίως ζουν με ΤΣΧΝΑ, ενώ ο αριθμός τους προβλέπεται να αυξηθεί στο μέλλον. Οι κύριοι παράγοντες αύξησης της επίπτωσης (δηλαδή του αριθμού των νεοεντασσόμενων ασθενών) της ΤΣΧΝΑ παγκοσμίως είναι η αύξηση της μέσης ηλικίας του πληθυσμού, η αλματώδης αύξηση του σακχαρώδους διαβήτη που λαμβάνει επιδημικές διαστάσεις, καθώς και οι αλλαγές στα ηλικιακά όρια για έναρξη θεραπείας. Στην Ελλάδα, κατά την περίοδο 2005-2009 το 74% ασθενών με ΤΣΧΝΑ έκανε αιμοκάθαρση, το 7% έκανε περιτοναϊκή κάθαρση και το 19% ζούσε με νεφρικό μόσχευμα. Το τελευταίο ποσοστό κατατάσσει την Ελλάδα στην 26η θέση ανάμεσα σε 36 χώρες παγκοσμίως όσον αφορά τον αριθμό των ασθενών που ζουν με μεταμοσχευμένο νεφρό. Η ανάλυση κόστους-αποτελεσματικότητας δείχνει πως η λιγότερο δαπανηρή θεραπεία της ΤΣΧΝΑ είναι η μεταμόσχευση, ακολουθούμενη από την περιτοναϊκή κάθαρση, ενώ η αιμοκάθαρση αναδεικνύεται ως η πιο δαπανηρή. Οι θεραπείες κατατάσσονται με την ίδια ακριβώς σειρά όσον αφορά και την ποιότητα ζωής που παρέχουν στους ασθενείς. Οι βασικές αιτίες για το χαμηλό ποσοστό μεταμοσχεύσεων στην Ελλάδα είναι η έλλειψη δωριζόμενων οργάνων, που οφείλεται κατά πολύ στην ελλιπή πληροφόρηση, η ανεπάρκεια του συστήματος διανομής οργάνων, ο υψηλός αριθμός ιδιωτικών κέντρων αιμοκάθαρσης, τα οποία δεν ενδιαφέρονται για μεταμοσχεύσεις, καθώς και κοινωνικοί παράγοντες. Ο σκοπός της παρούσας διατριβής ήταν η υλοποίηση ενός μοντέλου για την πρόβλεψη του αριθμού των ασθενών με ΤΣΧΝΑ στην Ελλάδα το 2020, καθώς επίσης και η διερεύνηση της επίδρασης διαφόρων σεναρίων αύξησης των μεταμοσχεύσεων. Επιπλέον, πραγματοποιήθηκε ανάλυση κόστους-αποτελεσματικότητας της αύξησης των μεταμοσχεύσεων. Η πρόβλεψη έγινε με βάση ένα μοντέλο Μαρκόφ. Τα μοντέλα Μαρκόφ διακρίνονται για την απλότητα αλλά και την ικανότητά τους να αναπαριστούν με ακρίβεια πολλά κλινικά προβλήματα. Για την παρούσα εργασία, υλοποιήθηκε ένα ντετερμινιστικό μοντέλο Μαρκόφ για την πρόβλεψη του μελλοντικού αριθμού των ασθενών με ΤΣΧΝΑ σε Θεραπεία Υποκατάστασης της Νεφρικής Λειτουργίας (ΘΥΝΛ) στην Ελλάδα. Επίσης, εφαρμόστηκαν τεχνικές Μόντε Κάρλο για μεγαλύτερη ευρωστία του μοντέλου. Ως εκ τούτου, υλοποιήθηκαν δύο διαφορετικά μοντέλα πρόβλεψης, ένα μοντέλο Μαρκόφ και ένα μοντέλο Μαρκόφ Μόντε Κάρλο. Για την υλοποίηση χρησιμοποιήθηκαν ηλικιακά δεδομένα (<45, 45-65, >65) του Ευρωπαϊκού Αρχείου Καταγραφής Νεφροπαθών (ERA-EDTA registry) που αφορούσαν νεοεντασσόμενους ασθενείς αλλά και ασθενείς που βρίσκονταν σε ΘΥΝΛ στην Ελλάδα την περίοδο 1998-2009. Ο σχεδιασμός μιας αλυσίδας Μαρκόφ βασίζεται στον πίνακα μετάβασης για υπολογισμό της πιθανότητας μετακίνησης του ασθενούς ανάμεσα στην Αιμοκάθαρση, την περιτοναϊκή κάθαρση, τη μεταμόσχευση και τον θάνατο. Για να υπολογιστούν οι πιθανότητες μετάβασης στην αλυσίδα Μαρκόφ, έγινε χρήση μιας επαναληπτικής τεχνικής μείωσης του σφάλματος με βάση τα ηλικιακά δεδομένα της περιόδου 1998-2006. Και τα δύο μοντέλα επαληθεύτηκαν επιτυχώς με βάση τα δεδομένα της περιόδου 2007-2009. Η πρόβλεψη του μελλοντικού αριθμού νεοεντασσόμενων ασθενών με ΤΣΧΝΑ στην Ελλάδα έγινε με διαφορετικό τρόπο σε κάθε μοντέλο. Στο μοντέλο Μαρκόφ, εφαρμόστηκαν τρία διαφορετικά σενάρια πρόβλεψης του ποσοστού επίπτωσης: χαμηλό, μεσαίο και υψηλό. Επιπλέον, σε κάθε μοντέλο ακολουθήθηκε διαφορετική προσέγγιση όσον αφορά την αύξηση του αριθμού των μεταμοσχεύσεων. Στο μοντέλο Μαρκόφ, εφαρμόστηκαν δύο σενάρια αύξησης των μεταμοσχεύσεων σε σχέση με τον αριθμό των ασθενών σε ΘΥΝΛ. Το πρώτο σενάριο βασίστηκε στην υπόθεση ότι ο μέσος αριθμός μεταμοσχεύσεων που έγιναν στην Ελλάδα κατά την περίοδο 2005-2009 θα διπλασιαστεί ως το 2020. Στο δεύτερο σενάριο θεωρήθηκε ότι η Ελλάδα θα φτάσει ως το 2020 το ποσοστό μεταμοσχεύσεων της Νορβηγίας το 2009, που ήταν το μεγαλύτερο παγκοσμίως για εκείνο το έτος. Στο μοντέλο Μαρκόφ Μόντε Κάρλο, η αύξηση του αριθμού των μεταμοσχεύσεων επιτεύχθηκε με ετήσια αύξηση κατά 1% του αριθμού των νεοεντασσόμενων ασθενών που θα έκαναν μεταμόσχευση, με αντίστοιχη μείωση του αριθμού των αιμοκαθαιρόμενων. Το μοντέλο Μαρκόφ προέβλεψε αύξηση του αριθμού των ασθενών σε ΘΥΝΛ στην Ελλάδα κατά 19.3%, 24.4% και 42.2% το 2020 σε σχέση με το 2009, ανάλογα με το εφαρμοζόμενο σενάριο επίπτωσης. Το μοντέλο Μαρκόφ Μόντε Κάρλο προέβλεψε αντίστοιχη αύξηση της τάξης του 25%. Στο μοντέλο Μαρκόφ, τα αποτελέσματα της αύξησης των μεταμοσχεύσεων έδειξαν ότι το 2020 θα υπάρξει αύξηση κατά 64.4% (πρώτο σενάριο) ή κατά 107.2% (δεύτερο σενάριο) του αριθμού των μεταμοσχευμένων ασθενών συγκριτικά με το 2009, με συνολική εξοικονόμηση 50.2 και 112.37 εκατομμύρια ευρώ αντίστοιχα, για την περίοδο 2010-2020. Τέλος, η αύξηση του αριθμού των μεταμοσχεύσεων στο μοντέλο Μαρκόφ Μόντε Κάρλο έδειξε αύξηση κατά 57.9% του αριθμού των ασθενών που ζουν με μεταμοσχευμένο νεφρό, με συνολική εξοικονόμηση 68.2 εκατομμύρια ευρώ. Τα αποτελέσματα και στα δύο μοντέλα καταδεικνύουν ότι η αύξηση του αριθμού των μεταμοσχεύσεων έναντι της αιμοκάθαρσης θα μείωνε το κόστος θεραπείας για το Σύστημα Υγείας της χώρας, ενώ ταυτοχρόνως θα βελτίωνε την ποιότητα ζωής για έναν σημαντικό αριθμό ασθενών με ΤΣΧΝΑ.

Cette thèse a pour objet l’étude de méthodes Bayésiennes pour l’analyse des ondes P et T des signaux ECG. Différents modèles statistiques et des méthodes Bayésiennes associées sont proposés afin de réaliser la détection des ondes P et T et leur caractérisation (détermination du sommet et des limites des ondes ainsi que l’estimation des formes d’onde). Ces modèles prennent en compte des lois a priori pour les paramètres inconnus (les positions des ondes, les amplitudes et les coefficients de ces formes d'onde) associés aux signaux ECG. Ces lois a priori sont ensuite combinées avec la vraisemblance des données observées pour fournir les lois a posteriori des paramètres inconnus. En raison de la complexité des lois a posteriori obtenues, des méthodes de Monte Carlo par Chaînes de Markov sont proposées pour générer des échantillons distribués asymptotiquement suivant les lois d’intérêt. Ces échantillons sont ensuite utilisés pour approcher les estimateurs Bayésiens classiques (MAP ou MMSE). D'autre part, pour profiter de la nature séquentielle du signal ECG, un modèle dynamique est proposé. Une méthode d'inférence Bayésienne similaire à celle développée précédemment et des méthodes de Monte Carlo séquentielles (SMC) sont ensuite étudiées pour ce modèle dynamique. Dans la dernière partie de ce travail, deux modèles Bayésiens introduits dans cette thèse sont adaptés pour répondre à un sujet de recherche clinique spécifique appelé détection de l'alternance des ondes T. Une des approches proposées a servi comme outil d'analyse dans un projet en collaboration avec St. Jude Medical, Inc et l'hôpital de Rangueil à Toulouse, qui vise à évaluer prospectivement la faisabilité de la détection des alternances des ondes T dans les signaux intracardiaques. / This thesis studies Bayesian estimation/detection algorithms for P and T wave analysis in ECG signals. In this work, different statistical models and associated Bayesian methods are proposed to solve simultaneously the P and T wave delineation task (determination of the positions of the peaks and boundaries of the individual waves) and the waveform-estimation problem. These models take into account appropriate prior distributions for the unknown parameters (wave locations and amplitudes, and waveform coefficients). These prior distributions are combined with the likelihood of the observed data to provide the posterior distribution of the unknown parameters. Due to the complexity of the resulting posterior distributions, Markov chain Monte Carlo algorithms are proposed for (sample-based) detection/estimation. On the other hand, to take full advantage of the sequential nature of the ECG, a dynamic model is proposed under a similar Bayesian framework. Sequential Monte Carlo methods (SMC) are also considered for delineation and waveform estimation. In the last part of the thesis, two Bayesian models introduced in this thesis are adapted to address a specific clinical research problem referred to as T wave alternans (TWA) detection. One of the proposed approaches has served as an efficient analysis tool in the Endocardial T wave Alternans Study (ETWAS) project in collaboration with St. Jude Medical, Inc and Toulouse Rangueil Hospital. This project was devoted to prospectively assess the feasibility of TWA detection in repolarisation on EGM stored in ICD memories.

Acquises dans plusieurs centaines de bandes spectrales contiguës, les images hyperspectrales permettent d'analyser finement la composition d'une scène observée. En raison de la résolution spatiale limitée des capteurs utilisés, le spectre d'un pixel d'une image hyperspectrale résulte de la composition de plusieurs signatures associées à des matériaux distincts. À ce titre, le démélange d'images hyperspectrales vise à estimer les signatures des différents matériaux observés ainsi que leur proportion dans chacun des pixels de l'image. Pour cette analyse, il est d'usage de considérer qu'une signature spectrale unique permet de décrire un matériau donné, ce qui est généralement intrinsèque au modèle de mélange choisi. Toutefois, la signature d'un matériau présente en pratique une variabilité spectrale qui peut être significative d'une image à une autre, voire au sein d'une même image. De nombreux paramètres peuvent en être cause, tels que les conditions d'acquisitions (e.g., conditions d'illumination locales), la déclivité de la scène observée ou des interactions complexes entre la lumière incidente et les éléments observés. À défaut d'être prises en compte, ces sources de variabilité perturbent fortement les signatures extraites, tant en termes d'amplitude que de forme. De ce fait, des erreurs d'estimation peuvent apparaître, qui sont d'autant plus importantes dans le cas de procédures de démélange non-supervisées. Le but de cette thèse consiste ainsi à proposer de nouvelles méthodes de démélange pour prendre en compte efficacement ce phénomène. Nous introduisons dans un premier temps un modèle de démélange original visant à prendre explicitement en compte la variabilité spatiale des spectres purs. Les paramètres de ce modèle sont estimés à l'aide d'un algorithme d'optimisation sous contraintes. Toutefois, ce modèle s'avère sensible à la présence de variations spectrales abruptes, telles que causées par la présence de données aberrantes ou l'apparition d'un nouveau matériau lors de l'analyse d'images hyperspectrales multi-temporelles. Pour pallier ce problème, nous introduisons une procédure de démélange robuste adaptée à l'analyse d'images multi-temporelles de taille modérée. Compte tenu de la dimension importante des données étudiées, notamment dans le cas d'images multi-temporelles, nous avons par ailleurs étudié une stratégie d'estimation en ligne des différents paramètres du modèle de mélange proposé. Enfin, ce travail se conclut par l'étude d'une procédure d'estimation distribuée asynchrone, adaptée au démélange d'un grand nombre d'images hyperspectrales acquises sur une même scène à différents instants. / Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have received an increasing interest due to the significant spectral information they convey about the materials present in a given scene. However, the limited spatial resolution of hyperspectral sensors implies that the observations are mixtures of multiple signatures corresponding to distinct materials. Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing the data -- referred to as endmembers -- and their relative proportion in each pixel according to a predefined mixture model. In this context, a given material is commonly assumed to be represented by a single spectral signature. This assumption shows a first limitation, since endmembers may vary locally within a single image, or from an image to another due to varying acquisition conditions, such as declivity and possibly complex interactions between the incident light and the observed materials. Unless properly accounted for, spectral variability can have a significant impact on the shape and the amplitude of the acquired signatures, thus inducing possibly significant estimation errors during the unmixing process. A second limitation results from the significant size of HS data, which may preclude the use of batch estimation procedures commonly used in the literature, i.e., techniques exploiting all the available data at once. Such computational considerations notably become prominent to characterize endmember variability in multi-temporal HS (MTHS) images, i.e., sequences of HS images acquired over the same area at different time instants. The main objective of this thesis consists in introducing new models and unmixing procedures to account for spatial and temporal endmember variability. Endmember variability is addressed by considering an explicit variability model reminiscent of the total least squares problem, and later extended to account for time-varying signatures. The variability is first estimated using an unsupervised deterministic optimization procedure based on the Alternating Direction Method of Multipliers (ADMM). Given the sensitivity of this approach to abrupt spectral variations, a robust model formulated within a Bayesian framework is introduced. This formulation enables smooth spectral variations to be described in terms of spectral variability, and abrupt changes in terms of outliers. Finally, the computational restrictions induced by the size of the data is tackled by an online estimation algorithm. This work further investigates an asynchronous distributed estimation procedure to estimate the parameters of the proposed models.

Submitted by JOÉL VILLAVICENCIO GASTELÚ null (tear_295@hotmail.com) on 2016-03-30T17:36:01Z No. of bitstreams: 1 Dissertação_Rev1_13 - Joel Gastelu.pdf: 3335802 bytes, checksum: 93fbe0689da0072cc77a9120a8e24b02 (MD5) / Rejected by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br), reason: Solicitamos que realize uma nova submissão seguindo as orientações abaixo: O arquivo submetido está sem a ficha catalográfica. A versão submetida por você é considerada a versão final da dissertação/tese, portanto não poderá ocorrer qualquer alteração em seu conteúdo após a aprovação. Corrija estas informações e realize uma nova submissão contendo o arquivo correto. Agradecemos a compreensão. on 2016-04-01T13:14:50Z (GMT) / Submitted by JOÉL VILLAVICENCIO GASTELÚ null (tear_295@hotmail.com) on 2016-04-01T19:04:22Z No. of bitstreams: 1 Dissertação_Joel.pdf: 4253690 bytes, checksum: 75d9921d8416eec7341f8bf0e2182766 (MD5) / Rejected by Ana Paula Grisoto (grisotoana@reitoria.unesp.br), reason: Solicitamos que realize uma nova submissão seguindo as orientações abaixo: A data informada na capa do documento está diferente da data de defesa que consta na ficha catalográfica e folha de aprovação. Corrija esta informação no arquivo PDF e realize uma nova submissão contendo o arquivo correto. Agradecemos a compreensão. on 2016-04-05T13:53:33Z (GMT) / Submitted by JOÉL VILLAVICENCIO GASTELÚ null (tear_295@hotmail.com) on 2016-04-06T22:35:57Z No. of bitstreams: 1 Dissertação_Joel.pdf: 4231140 bytes, checksum: 4bd6143a52dc3a6846abd4f996ba9306 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-04-07T12:21:23Z (GMT) No. of bitstreams: 1 gastelu_jv_me_ilha.pdf: 4231140 bytes, checksum: 4bd6143a52dc3a6846abd4f996ba9306 (MD5) / Made available in DSpace on 2016-04-07T12:21:23Z (GMT). No. of bitstreams: 1 gastelu_jv_me_ilha.pdf: 4231140 bytes, checksum: 4bd6143a52dc3a6846abd4f996ba9306 (MD5) Previous issue date: 2016-03-01 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / No presente trabalho tem-se como objetivo estimar o potencial fotovoltaico devido à instalação de sistemas fotovoltaicos em telhados de áreas residenciais. Na estimação desse potencial foram consideradas quatro grandezas: o nível de irradiação solar, a área aproveitável de telhado para a instalação dos sistemas fotovoltaicos, a eficiência de conversão dos sistemas fotovoltaicos e as probabilidades de instalação dos sistemas fotovoltaicos, que caracterizam as preferências dos habitantes à instalação desses sistemas. Um modelo hierárquico bayesiano foi proposto para o cálculo das probabilidades de instalação dos sistemas fotovoltaicos. Nesse modelo bayesiano é estabelecida uma relação entre as probabilidades de instalação, as variáveis socioeconômicas e as interações entre as subáreas, através de um modelo linear generalizado misto. O cálculo do valor esperado das probabilidades de instalação foi realizado usando o método de Monte Carlo via cadeias de Markov. Os resultados do potencial fotovoltaico são apresentados através de mapas temáticos, que permitem a visualização da distribuição espacial do seu valor esperado. Esta informação pode ajudar as concessionárias de distribuição no planejamento e expansão de suas redes elétricas em regiões com maior potencial de geração fotovoltaica. / The present work aims to estimate the photovoltaic potential for installing solar panel on the rooftop of residential areas. The estimation of this potential considers four quantities: the solar radiation level, rooftop availability for installation of photovoltaic systems, conversion efficiency of the photovoltaic systems and the probabilities for the installation of photovoltaic systems that characterize the preferences of the inhabitants to the installation of such systems. A bayesian hierarchical model is proposed to calculate the installation probabilities of photovoltaic systems. This bayesian model establishes a relation among the installation probabilities, socioeconomic variables and interactions between subareas, through a generalized linear mixed model. The calculation of expected value of installation probabilities in each subarea is performed using the Markov Chain Monte Carlo method. Photovoltaic potential results are presented through thematic maps that allow the visualization of the spatial distribution of its expected value. This information can help to distribution utilities for planning and expansion of their networks in regions with the greatest potential for photovoltaic generation.

Orientador: Antônio Padilha Feltrin / Resumo: No presente trabalho tem-se como objetivo estimar o potencial fotovoltaico devido à instalação de sistemas fotovoltaicos em telhados de áreas residenciais. Na estimação desse potencial foram consideradas quatro grandezas: o nível de irradiação solar, a área aproveitável de telhado para a instalação dos sistemas fotovoltaicos, a eficiência de conversão dos sistemas fotovoltaicos e as probabilidades de instalação dos sistemas fotovoltaicos, que caracterizam as preferências dos habitantes à instalação desses sistemas. Um modelo hierárquico bayesiano foi proposto para o cálculo das probabilidades de instalação dos sistemas fotovoltaicos. Nesse modelo bayesiano é estabelecida uma relação entre as probabilidades de instalação, as variáveis socioeconômicas e as interações entre as subáreas, através de um modelo linear generalizado misto. O cálculo do valor esperado das probabilidades de instalação foi realizado usando o método de Monte Carlo via cadeias de Markov. Os resultados do potencial fotovoltaico são apresentados através de mapas temáticos, que permitem a visualização da distribuição espacial do seu valor esperado. Esta informação pode ajudar as concessionárias de distribuição no planejamento e expansão de suas redes elétricas em regiões com maior potencial de geração fotovoltaica. / Abstract: The present work aims to estimate the photovoltaic potential for installing solar panel on the rooftop of residential areas. The estimation of this potential considers four quantities: the solar radiation level, rooftop availability for installation of photovoltaic systems, conversion efficiency of the photovoltaic systems and the probabilities for the installation of photovoltaic systems that characterize the preferences of the inhabitants to the installation of such systems. A bayesian hierarchical model is proposed to calculate the installation probabilities of photovoltaic systems. This bayesian model establishes a relation among the installation probabilities, socioeconomic variables and interactions between subareas, through a generalized linear mixed model. The calculation of expected value of installation probabilities in each subarea is performed using the Markov Chain Monte Carlo method. Photovoltaic potential results are presented through thematic maps that allow the visualization of the spatial distribution of its expected value. This information can help to distribution utilities for planning and expansion of their networks in regions with the greatest potential for photovoltaic generation. / Mestre

Il y a de nouveaux défis dans l'administration et dans la conception des systèmes pour optimiser la gestion des ressources des applications basées en nuage Cloud Computing. Certaines applications demandent des performances rigoureuses (par exemple, par rapport aux retards et aux limites de la gigue), tandis que d'autres applications présentent des charges de travail en rafale (volatiles). Cette thèse propose un framework inspiré dans un modèle épidémique (et basé sur des Chaînes de Markov à Temps Continu), qui peut reproduire la volatilité de la charge de travail, à savoir les effets de buzz (quand il y a une augmentation soudaine de la popularité d'un contenu) d'un système de Vidéo à la Demande (VoD). Deux méthodes d'estimation (basés sur des heuristiques et des Chaînes de Markov Monte Carlo - MCMC) ont été également proposées dans ce travail, de façon à ajuster le modèle selon les comportements de la charge de travail. Les paramètres du modèle obtenus à partir des procédures d'étalonnage révèlent des propriétés intéressantes du modèle. Basé sur des simulations numériques, la précision des deux procédures a été analysée, en montrant que les deux présentent des performances raisonnables. Toutefois, la méthode MCMC dépasse la performance de l'approche heuristique. Cette thèse compare également le modèle proposé avec d'autres modèles existants, tout en examinant la qualité de l'ajustement de certaines propriétés statistiques sur des traces réelles de la charge de travail. Finalement, ce travail propose une approche probabiliste de provisionnement des ressources, basée sur le Principe de Grandes Déviations (LDP). LDP caractérise statistiquement les effets de buzz, qui causent de la volatilité extrême de la charge de travail. Cette analyse exploite les informations obtenues en utilisant le LPD du système VoD pour la définition des politiques de gestion des ressources. Ces politiques peuvent être intéressantes pour toutes les acteurs dans le nouveau contexte de l'informatique en nuage. / There are some new challenges in system administration and design to optimize the resource management for a cloud based application. Some applications demand stringent performance requirements (e.g. delay and jitter bounds), while some applications exhibit bursty (volatile) workloads. This thesis proposes an epidemic model inspired (and continuous time Markov Chain based) framework, which can reproduce workload volatility namely the "buzz effects" (when there is a sudden increase of a content popularity) of a Video on Demand (VoD) system. Two estimation procedures (heuristic and a Markov Chain Monte Carlo (MCMC) based approach) have also been proposed in this work to calibrate the model against workload traces. Obtained model parameters from the calibration procedures reveal some interesting property of the model. Based on numerical simulations, precisions of both procedures have been analyzed, which show that both of them perform reasonably. However, the MCMC procedure outperforms the heuristic approach. This thesis also compares the proposed model with other existing models examining the goodness-of-fit of some statistical properties of real workload traces. Finally this work suggests a probabilistic resource provisioning approach based on a Large Deviation Principle (LDP). LDP statistically characterizes the buzz effects that causeextreme workload volatility. This analysis exploits the information obtained using the LDP of the VoD system for defining resource management policies. These policies may be of some interest to all stakeholders in the emerging context of cloud networking.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-07T12:44:03Z No. of bitstreams: 1 Josimar Mendes de Vasconcelos.pdf: 4725831 bytes, checksum: 716f4b6bc6100003772271db252915b7 (MD5) / Made available in DSpace on 2016-07-07T12:44:03Z (GMT). No. of bitstreams: 1 Josimar Mendes de Vasconcelos.pdf: 4725831 bytes, checksum: 716f4b6bc6100003772271db252915b7 (MD5) Previous issue date: 2011-08-08 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The last years has increased the quantity of researchers and search scientific in the plantation, production and value of the soybeans in the Brazil, in grain. In front of this, the present dissertation looks for to analyze the data and estimate models that explain, of satisfactory form, the variability observed of the quantity produced and value of the production of soya in grain in the Brazil, in the field of the study. For the development of these analyses is used the classical and Bayesian inference, in the context of simultaneous equations by the tools of indirect square minimum in two practices. In the classical inference uses the estimator of square minima in two practices. In the Bayesian inference worked the method of Mountain Carlo via Chain of Markov with the algorithms of Gibbs and Metropolis-Hastings by means of the technician of simultaneous equations. In the study, consider the variable area harvested, quantity produced, value of the production and gross inner product, in which it adjusted the model with the variable answer quantity produced and afterwards the another variable answer value of the production for finally do the corrections and obtain the final result, in the classical and Bayesian method. Through of the detours normalized, statistics of the proof-t, criteria of information Akaike and Schwarz normalized stands out the good application of the method of Mountain Carlo via Chain of Markov by the algorithm of Gibbs, also is an efficient method in the modelado and of easy implementation in the statistical softwares R & WinBUGS, as they already exist smart libraries to compile the method. Therefore, it suggests work the method of Mountain Carlo via chain of Markov through the method of Gibbs to estimate the production of soya in grain. / Nos últimos anos tem aumentado a quantidade de pesquisadores e pesquisas científicas na plantação, produção e valor de soja no Brasil, em grão. Diante disso, a presente dissertação busca analisar os dados e ajustar modelos que expliquem, de forma satisfatória, a variabilidade observada da quantidade produzida e valor da produção de soja em grão no Brasil, no campo do estudo. Para o desenvolvimento dessas análises é utilizada a inferência clássica e bayesiana, no contexto de equações simultâneas através da ferramenta de mínimos quadrados em dois estágios. Na inferência clássica utiliza-se o estimador de mínimos quadrados em dois estágios. Na inferência bayesiana trabalhou-se o método de Monte Carlo via Cadeia de Markov com os algoritmos de Gibbs e Metropolis-Hastings por meio da técnica de equações simultâneas. No estudo, consideram-se as variáveis área colhida, quantidade produzida, valor da produção e produto interno bruto, no qual ajustou-se o modelo com a variável resposta quantidade produzida e depois a variável resposta valor da produção para finalmente fazer as correções e obter o resultado final, no método clássico e bayesiano. Através, dos desvios padrão, estatística do teste-t, critérios de informação Akaike e Schwarz normalizados destaca-se a boa aplicação do método de Monte Carlo via Cadeia de Markov pelo algoritmo de Gibbs, também é um método eficiente na modelagem e de fácil implementação nos softwares estatísticos R & WinBUGS, pois já existem bibliotecas prontas para compilar o método. Portanto, sugere-se trabalhar o método de Monte Carlo via cadeia de Markov através do método de Gibbs para estimar a produção de soja em grão, no Brasil.

[pt] A dissertação trata da comparação de dois métodos de estimação para modelos de séries temporais com volatilidade estocástica. Um dos métodos é baseado em inferência Bayesiana e depende de simulações enquanto o outro utiliza máxima verossimilhança para o processo de estimação. A comparação é feita tanto com séries temporais artificialmente geradas como também com séries financeiras reais. O objetivo é mostrar que os dois métodos apresentam resultados semelhantes, sendo que o segundo método é significativamente mais rápido do que o primeiro.