Chen(2010)提出如何用勝率函數來判斷給定的連續條件分配是否相容，以及 相容時如何求對應的聯合分配。本研究提出，在二維有限的情形下，如何用勝率 矩陣來判斷給定的條件機率矩陣是否相容，以及相容時如何求對應的聯合機率矩 陣。又給定的條件機率矩陣不相容時，我們介紹了四種修改勝率矩陣的方法，同 時在使用幾何平均法調整勝率矩陣的過程中，也發現選取最佳參考點以獲得最佳 近似聯合機率矩陣之方法，並且給予理論證明。最後以模擬的方式發現，在修改 勝率矩陣的四種方法中，以幾何平均法所得到的近似聯合機率矩陣，其條件機率 矩陣最常接近所給定的條件機率矩陣。 / Chen (2010) provides the representations of odds ratio function to examine the compatibility of conditional probability density functions and gives the corresponding joint probability density functions if they are compatible. In this research, we provide the representations of odds ratio matrix to examine the compatibility of two discrete conditional probability matrices and give the corresponding joint probability matrix if they are compatible. For incompatible situations, we offer four methods to revise odds ratio matrices to find near joint probability matrices so that their conditional probability matrices are not far from the two given ones. That is, we provide four methods so that the sums of error squares are small. For each method, the sum of error squares may depend on the same reference point of two odds ratio matrices. We first discover by example that only the geometric method out of these four methods has a pattern to get the best reference point so that the sum of error squares is smallest. We then prove this finding in general. In addition, through simulation results, the geometric method would provide the smallest sum of error squares most often among these four methods. Hence, we suggest using geometric method. Its strategy to find the best reference point is also given.

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This bachelor’s thesis is a combined study of applied mathematical statistics and industrial engineering and management implemented to develop an algorithm which creates product combinations based on customer data analysis for eleven AB. Mathematically, generalized linear modelling, combinatorics and conditional probabilities were applied to create sales prediction models, generate potential combinations and calculate the conditional probabilities of the combinations getting purchased. SWOT analysis was used to identify which factors can enhance the sales from an industrial engineering and management perspective. Based on the regression analysis, the study showed that the considered variables, which were sales prices, brands, ratings, purchase countries, purchase months and how new the products are, affected the sales amounts of the products. The algorithm takes a barcode of a product as an input and checks whether if the corresponding product type satisfies the requirements of predicted sales amount and conditional probability. The algorithm then returns a list of possible product combinations that fulfil the recommendations. / Detta kandidatexamensarbete är en kombinerad studie av tillämpad matematisk statistik och industriell ekonomisk implementering för att utveckla en algoritm som skapar produktkombinationer baserad på kunddata analys för eleven AB. I den matematiska delen tillämpades generaliserade linjära modeller, kombinatorik och betingade sannolikheter för att skapa prediktionsmodeller för försäljningsantal, generera potentiella kombinationer och beräkna betingade sannolikheter att kombinationerna bli köpta. SWOT-analys användes för att identifiera vilka faktorer som kan öka försäljningen från ett industriell ekonomiskt perspektiv. Baserat på regressionsanalysen, studien har visat att de betraktade variablerna, som var försäljningspriser, varumärken, försäljningsländer, försäljningsmånader och hur nya produkterna är, påverkade försäljningsantalen på produkterna. Algoritmen tar emot en streckkod av en produkt som inmatning och kontrollerar om den motsvarande produkttypen uppfyller kraven för predikterad försäljningssumma och betingad sannolikhet. Algoritmen returnerar en lista av alla möjliga kombinationer på produkter som uppfyller rekommendationerna.

Bayesian networks can be used to model the respiratory system. Their structure indicate how risk factors, symptoms, and diseases are related and the Conditional Probability Tables enable predictions about a patient’s need for hospitalization. Numerous structure learning algorithms exist for discerning the structure of a Bayesian network, but none can guarantee to find the perfect structure. Employing multiple algorithms can discover relationships between variables that might otherwise remain hidden when relying on a single algorithm. The Maximum Likelihood Estimator is the predominant algorithm for learning the Conditional Probability Tables. However, it faces challenges due to the data fragmentation problem, which can compromise its predictions. Failing to hospitalize patients who require specialized medical care could lead to severe consequences. Therefore, in this thesis, the use of an XGBoost model for learning is proposed as a novel and better method since it does not suffer from data fragmentation. A Bayesian network is constructed combining several structure learning algorithms, and the predictive performance of the Maximum Likelihood Estimator and XGBoost are compared. XGBoost achieved a maximum accuracy of 86.0% compared to the Maximum Likelihood Estimator, which attained an accuracy of 81.5% in predicting future patient hospitalization. In this way, the predictive performance of Bayesian networks has been enhanced. / Bayesianska nätverk kan användas för att modellera andningssystemet. Deras struktur visar hur riskfaktorer, symtom och sjukdomar är relaterade, och de villkorliga sannolikhetstabellerna möjliggör prognoser om en patients behov av sjukhusvård. Det finns många strukturlärningsalgoritmer för att urskilja strukturen i ett bayesianskt nätverk, men ingen kan garantera att hitta den perfekta strukturen. Genom att använda flera algoritmer kan man upptäcka relationer mellan variabler som annars kan förbli dolda när man bara förlitar sig på en enda algoritm. Maximum Likelihood Estimator är den dominerande algoritmen för att lära sig de villkorliga sannolikhetstabellerna. Men den står inför utmaningar på grund av datafragmenteringsproblemet, vilket kan äventyra dess prognoser. Att inte lägga in patienter som behöver specialiserad medicinsk vård kan leda till allvarliga konsekvenser. Därför föreslås i denna avhandling användningen av en XGBoost-modell för inlärning som en ny och bättre metod eftersom den inte lider av datafragmentering. Ett bayesianskt nätverk byggs genom att kombinera flera strukturlärningsalgoritmer, och den prediktiva prestandan för Maximum Likelihood Estimator och XGBoost jämförs. XGBoost uppnådde en maximal noggrannhet på 86,0% jämfört med Maximum Likelihood Estimator, som uppnådde en noggrannhet på 81,5% för att förutsäga framtida patientinläggning. På detta sätt har den prediktiva prestandan för bayesianska nätverk förbättrats.

Made available in DSpace on 2016-04-27T16:58:17Z (GMT). No. of bitstreams: 1 Auriluci C Figueiredo.pdf: 465672 bytes, checksum: 1df03500d906e4b187451daa2641f78b (MD5) Previous issue date: 2000-12-12 / This work has for objective to introduce the concept of the Conditional Probability in courses of Statistics in the University. For that we elaborated, we applied and we analyzed the results of a teaching sequence taking in consideration the beginnings of a Didactic Engineering. This teaching sequence is composed of four activities that they were created, basing on Carmen Batanero's didactic situations and other, with the aim of doing the student to contemplate on circumstances that involve not only the Conditional Probability, as the concepts linked to the Theorem of the Total Probability and the Theorem of Bayes. To work with such concepts we articulated in the subjects of the activities different representation registrations: natural, symbolic language, diagram of trees and contingency table, taking as base the Theory of Registrations of Representation of Raymond Duval. We applied this sequence to students of the courses of Licentiate of Mathematics and Science of the Computation and before the those students' protocols, we concluded that our sequence aided them they decrease it the difficulties lifted by us and for the researchers and at the same time it indicated themes for future researches in the area. Enter other conclusions stood out that most of the students before subjects that involve the Conditional Probability differentiated this of the Probability of the intersection of events and I calculate it of P(A/B) of the one of P(B/A), since these if they come in the questions in natural language. However when similar subjects were presented in the language symbolic many students they showed difficulties in solving them / Este trabalho tem por objetivo introduzir o conceito da Probabilidade Condicional em cursos de Estatística na Universidade. Para isso, elaboramos, aplicamos e analisamos os resultados de uma seqüência de ensino levando em consideração os princípios de uma Engenharia Didática. Esta seqüência de ensino é composta de quatro atividades que foram criadas, baseando-se nas situações didáticas apresentadas por Carmen Batanero e outros autores, com o intuito de fazer o aluno refletir sobre circunstâncias que envolvam não só a Probabilidade Condicional, bem como os conceitos ligados ao Teorema da Probabilidade Total e o Teorema de Bayes. Para trabalhar com tais conceitos, articulamos nas questões das atividades, diferentes registros de representação: linguagem natural, simbólica, diagrama de árvore e tabela de contingência, tomando como base a Teoria de Registros de Representação de Raymond Duval. Aplicamos esta seqüência aos alunos dos cursos de Licenciatura de Matemática e Ciência da Computação e diante dos protocolos desses alunos, concluímos que nossa seqüência os auxiliou a minimizar as dificuldades levantadas por nós e pelos pesquisadores e ao mesmo tempo indicou temas para futuras pesquisas na área. Dentre outras conclusões, ressaltamos que a maioria dos alunos diante de questões que envolvam a Probabilidade Condicional diferenciavam esta da Probabilidade da Interseção de Eventos e o Cálculo da P(A/B) do de P(B/A), desde que estes se apresentassem nas perguntas em linguagem natural. No entanto, quando questões análogas foram apresentadas na linguagem simbólica, muitos alunos mostraram dificuldades em resolvê-las

Les modèles de Rejets Urbains par Temps de Pluie (MRUTP) de Matières en Suspension (MES) dans les systèmes d’assainissement urbains sont essentiels pour des raisons scientifiques, environnementales, opérationnelles et réglementaires. Néanmoins, les MRUTP ont été largement mis en question, surtout pour reproduire des données mesurées en continu à l’exutoire des grands bassins versants. Dans cette thèse, trois limitations potentielles des MRUTP traditionnels ont été étudiées dans un bassin versant de 185 ha (Chassieu, France), avec des mesures en ligne de 365 événements pluvieux : a) incertitudes des données dû aux conditions sur le terrain, b) incertitudes dans les modèles hydrologiques et mesures de pluie et c) incertitudes dans les structures traditionnelles des MRUTP. Ces aspects sont approfondis dans six apports séparés, dont leurs résultats principaux peuvent être synthétisés comme suites : a) Acquisition et validation des données : (i) quatre stratégies d’échantillonnage pendant des événements pluvieux sont simulées et évaluées à partir de mesures en ligne de MES et débit. Les intervalles d’échantillonnage recommandés sont de 5 min, avec des erreurs moyennes entre 7 % et 20 % et des incertitudes sur ces erreurs d’environ 5 %, selon l’intervalle d’échantillonnage; (ii) la probabilité de sous-estimation de la concentration moyenne dans la section transversale du réseau est estimée à partir de deux méthodologies. Une méthodologie montre des sous-estimations de MES plus réelles (vers 39 %) par apport à l'autre (vers 269 %). b) Modèles hydrologiques et mesures de pluie : (iii) une stratégie d’estimation de paramètres d’un modèle conceptuel pluie-débit est proposée, en analysant la variabilité des paramètres optimaux obtenus à partir d’un calage Bayésien évènement-par-évènement; (iv) une méthode pour calculer les précipitations moyennes sur un bassin versant est proposée, sur la base du même modèle hydrologique et les données de débit. c) MRUTP (pollutographes) : (v) la performance de modélisation à partir du modèle traditionnel courbe d’étalonnage (RC) a été supérieur aux différents modèles linéaires de fonctions de transfert (TF), surtout en termes de parcimonie et précision des simulations. Aucune relation entre les potentielles erreurs de mesure de la pluie et les conditions hydrologiques définies en (iii) et (iv) avec les performances de RC et TFs n’a pu être établie. Des tests statistiques renforcent que l’occurrence des évènements non-représentables par RC ou TF au cours de temps suit une distribution aléatoire (indépendante de la période sèche précédente); (vi) une méthode de reconstruction Bayésienne de variables d’état virtuelles indique que des processus potentiellement manquants dans une description RC sont ininterprétables en termes d’un unique état virtuel de masse disponible dans le bassin versant qui diminue avec le temps, comme nombre de modèles traditionnels l’ont supposé. / Total Suspended Solids (TSS) stormwater models in urban drainage systems are often required for scientific, legal, environmental and operational reasons. However, these TSS stormwater traditional model structures have been widely questioned, especially when reproducing data from online measurements at the outlet of large urban catchments. In this thesis, three potential limitations of traditional TSS stormwater models are analyzed in a 185 ha urban catchment (Chassieu, Lyon, France), by means 365 rainfall events monitored online: a) uncertainties in TSS data due to field conditions; b) uncertainties in hydrological models and rainfall measurements and c) uncertainties in the stormwater quality model structures. These aspects are investigated in six separate contributions, whose principal results can be summarized as follows: a) TSS data acquisition and validation: (i) four sampling strategies during rainfall events are simulated and evaluated by online TSS and flow rate measurements. Recommended sampling time intervals are of 5 min, with average sampling errors between 7 % and 20 % and uncertainties in sampling errors of about 5 %, depending on the sampling interval; (ii) the probability of underestimating the cross section mean TSS concentration is estimated by two methodologies. One method shows more realistic TSS underestimations (about 39 %) than the other (about 269 %). b) Hydrological models and rainfall measurements: (iii) a parameter estimation strategy is proposed for conceptual rainfall-runoff model by analyzing the variability of the optimal parameters obtained by single-event Bayesian calibrations, based on clusters and graphs representations. The new strategy shows more performant results in terms of accuracy and precision in validation; (iv) a methodology aimed to calculate “mean” areal rainfall estimation is proposed, based on the same hydrological model and flow rate data. Rainfall estimations by multiplying factors over constant-length time window and rainfall zero records filled with a reverse model show the most satisfactory results compared to further rainfall estimation models. c) Stormwater TSS pollutograph modelling: (v) the modelling performance of the traditional Rating Curve (RC) model is superior to different linear Transfer Function models (TFs), especially in terms of parsimony and precision of the simulations. No relation between the rainfall corrections or hydrological conditions defined in (iii) and (iv) with performances of RC and TFs could be established. Statistical tests strengthen that the occurrence of events not representable by the RC model in time is independent of antecedent dry weather conditions; (vi) a Bayesian reconstruction method of virtual state variables indicate that potential missing processes in the RC description are hardly interpretable as a unique state of virtual available mass over the catchment decreasing over time, as assumed by a great number of traditional models.

This thesis deals with the synthesis of anisotropic volumetric textures from a single 2D observation. We present variants of non parametric and multi-scale algorithms. Their main specificity lies in the fact that the 3D synthesis process relies on the sampling of a single 2D input sample, ensuring consistency in the different views of the 3D texture. Two types of approaches are investigated, both multi-scale and based on markovian hypothesis. The first category brings together a set of algorithms based on fixed-neighbourhood search, adapted from existing algorithms of texture synthesis from multiple 2D sources. The principle is that, starting from a random initialisation, the 3D texture is modified, voxel by voxel, in a deterministic manner, ensuring that the grey level local configurations on orthogonal slices containing the voxel are similar to configurations of the input image. The second category points out an original probabilistic approach which aims at reproducing in the textured volume the interactions between pixels learned in the input image. The learning is done by non-parametric Parzen windowing. Optimization is handled voxel by voxel by a deterministic ICM type algorithm. Several variants are proposed regarding the strategies used for the simultaneous handling of the orthogonal slices containing the voxel. These synthesis methods are first implemented on a set of structured textures of varied regularity and anisotropy. A comparative study and a sensitivity analysis are carried out, highlighting the strengths and the weaknesses of the different algorithms. Finally, they are applied to the simulation of volumetric textures of carbon composite materials, on nanometric scale snapshots obtained by transmission electron microscopy. The proposed experimental benchmark allows to evaluate quantitatively and objectively the performances of the different methods.

With the prevalence of internet, it is gradually becoming a trend to use the network as a tool of computer-added education. However, it is used to present the computer-added education with static state of the word, but it is just convenient to read for the user and there are no difference with traditional textbook. As the growing up of WWW and the development of Java, the interactive computer-added education is becoming a trend in the future and it can promote the effect of teaching basic statistics with the application of this new media. The instructor can take advantage of HTML by combining with Java Applets to achieve the display of interactive education through WWW. In this paper, we will use six examples of Java Applets about statistical computer-added education to help student easily to learn and to understand some abstract statistical concepts. The key methods to reach the goal are visualization and simulation with the display of graphics or games. Finally, we will discuss how to use the Applets and how to add the Java Applets into your homepage easily.

給定聯合分配，可以容易地導出對應的條件分配。反之，給定條件分配的資訊，是否能導出對應的聯合分配呢？例如根據O. Paul et al.（1963，1968）對造成心血管疾病因素之追蹤研究，可得出咖啡量、吸菸量及是否有心血管疾病三者間的條件機率模型資料，是否能找到對應的聯合機率模型，以便可以更深入地研究三者之關係，是一個重要的議題。在選定參考點下，Chen（2010）提出以勝算比法找條件密度函數相容的充要條件，以及在相容性成立時，如何求得聯合分配。在二維中，當兩正值條件機率矩陣不相容時，郭俊佑（2013）以幾何平均法修正勝算比矩陣，並導出近似聯合分配，同時利用幾何平均法之特性，提出最佳參考點之選擇法則。本研究以二維的勝算比法為基礎，探討三維離散的相容性問題，獲得下列幾項結果：一、證明了三個三維條件機率矩陣相容的充要條件就是兩兩相容。二、當三維條件機率矩陣不相容時，利用幾何平均法導出近似聯合分配。三、利用兩兩相容的充要條件，導出三維條件機率矩陣相容的充要條件，並證明該充要條件與Chen的結果一致。四、在幾何平均法下，提出最少點法，有效率地找出最佳參考點，以產生總誤差最小的近似聯合分配。五、設計出程式檢驗三維條件機率矩陣是否相容，並找出最佳參考點，同時比較最少點法與窮舉法之間效率的差異。 / Given a joint distribution, we can easily derive the corresponding fully conditional distributions. Conversely, given fully conditional distributions, can we find out the corresponding joint distribution? For example, according to a longitudinal study of coronary heart disease risk factors by O. Paul et al. (1963, 1968), we obtain conditional probability model data among coffee intake, the number of cigarettes smoked and whether he/she has coronary heart disease or not. Whether we can find out the corresponding joint distribution is an important issue as the joint distribution may be used to do further analyses. Chen (2010) used odds ratio method to find a necessary and sufficient condition for their compatibility and also gave the corresponding joint distribution for compatible situations. When two positive discrete conditional distributions in two dimensions are incompatible, Kuo (2013) used a geometric mean method to modify odds ratio matrices and derived an approximate joint distribution. Kuo also provided a rule to find the best reference point when the geometric mean method is used. In this research, based on odds ratio method in two dimensions, we discuss their compatibility problems and obtain the following results on three-dimensional discrete cases. Firstly, we prove that a necessary and sufficient condition for the compatibility of three conditional probability matrices in three dimensions is pairwise compatible. Secondly, we extend Kuo’s method on two-dimensional cases to derive three-dimensional approximate joint distributions for incompatible situations. Thirdly, we derive a necessary and sufficient condition for the compatibility of three conditional probability matrices in three dimensions in terms of pairwise compatibility and also prove that this condition is consistent with Chen’s results. Fourthly, we provide a minimum-points method to efficiently find the best reference point and yield an approximate joint distribution such that total error is the smallest. Fifthly, we design a computer program to run three-dimensional discrete conditional probability matrices problems for compatibility and also compare the efficiency between minimum-points method and exhausting method.

Este trabalho tem como objetivo introduzir os conceitos bÃsicos da Teoria das Probabilidades e apresentar noÃÃes sobre alguns modelos probabilÃsticos para o estudante do Ensino MÃdio. Iniciaremos o trabalho apresentando no capÃtulo 1 as noÃÃes de experimento determinÃstico, experimento aleatÃrio, espaÃo amostral e eventos, seguidos de algumas definiÃÃes de Probabilidade, conceitos que constituem a base para essa teoria. No capÃtulo 2 abordaremos os conceitos de Probabilidade Condicional e IndependÃncia de Eventos, apresentando alguns teoremas importantes que decorrem desses conceitos, bem como algumas de suas aplicaÃoes. No capÃtulo 3 apresentaremos de maneira simples alguns modelos probabilÃsticos discretos bastante Ãteis por modelarem de forma eficaz um bom nÃmero de experimentos aleatÃrios contribuindo assim para o cÃlculo das probabilidades de seus resultados. Por fim, no capÃtulo 4 serà apresentado o modelo probabilÃstico conhecido como DistribuiÃÃo de Poisson, que nos permite calcular a probabilidade de um evento ocorrer em um dado intervalo de tempo ou numa dada regiÃo espacial. / This work has as objective introduce the basic concepts of the Theory of Probabilities and present notions on some probabilistic models for the student of the High School. We will begin the work presented in chapter I the notions of experiment deterministic, random experiment, sample space and events, followed by some definitions of Probability concepts that constitute the basis for this theory. In chapter II we will discuss the concepts of Conditional Probability and Independence of Events showcasing some important theorems that derive from these concepts, as well as some of its applications. In chapter III we will present in a simple way some probabilistic models discrete quite useful for shape effectively a good number of random experiments thus contributing to the calculation of the probabilities of its results. Finally, in chapter IV will be presented the probability model known as Poisson distribution, which allows us to calculate the probability that an event will occur in a given time interval or in a given spatial region.