Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. Description based on print version record. Includes bibliographical references.

Let $mathcal{B}$ be a separable Banach space. In this thesis, it is shown that the Chung's strong law of large numbers holds for a sequence of independent $mathcal{B}$-valued random elements and an array of rowwise independent $mathcal{B}$-valued random elements under some weaker assumptions by using more generalized functions $phi_{n}$'s.

In this thesis, we use martingales to show that the dilation of a sequence of monotone convolutions $D_\frac{1}{b_n} (\mu_1 \triangleright \mu_2 \triangleright \cdots \triangleright \mu_n)$ is stable, where $\mu_j$ are probability distributions with the condition $\sum \limits_{n=1}^\infty \frac{1}{b_n} \text{var}(\mu_n) < \infty$. This proves a law of large numbers for monotonically independent random variables.

Quase três séculos já se passaram desde que a primeira versão do chamado paradoxo de São Petersburgo chegou aos meios acadêmicos através do trabalho de Daniel Bernoulli. Contudo, a relevância desse assunto ainda reverbera em artigos científicos atuais em diversas áreas do conhecimento (notadamente, mas não exclusivamente, na Economia e na Estatística). Um jogo de enunciado simples cuja esperança matemática dos ganhos do jogador surpreendentemente é infinita, entretanto, dificilmente alguém estaria disposto a pagar qualquer taxa de entrada cobrada para jogá-lo. No presente trabalho buscou-se em primeiro lugar apresentar uma análise crítica do desenvolvimento histórico das \"soluções\" propostas para o paradoxo. Em seguida mostrou-se uma aplicação direta do paradoxo a um modelo matemático utilizado até hoje para avaliar o preço justo de ações. Por fim, revisaram-se alguns resultados obtidos pela moderna teoria da probabilidade através da convergência em probabilidade. / It has been almost three centuries since the first version of the so-called St. Petersburg Paradox has reached the academic environment through the work of Daniel Bernoulli. However, the relevance of this subject still reverberates in new scientific papers in many knowledge fields (especially, but not exclusively, in Economics and Statistics). A game with a simple rule in which the mathematical expectation of the player\'s gains is unexpectedly infinite but hardly someone would be willing to pay any asked entrance fee to play it. In this work we pursued at first to present a critical analysis on the historical development of the proposed \"solutions\" to the paradox. After that, we showed an application of the paradox to a mathematical model, that is still in use today, to obtain a fair price of a stock share. At last we reviewed some results given by the modern probability theory through the convergence in probability.

One of the most remarkable theories in probability and statistics is the law of large numbers. Law of large numbers describes the behavior of random phenomena when they are reiterated infinitely or in very large trials. Apart from the mathematical exposition of the law of large numbers, its theory and applications have been widely used in gambling houses, financial sectors, and healthcare insurance where uncertainties deteriorate prediction and financial strength. However, the applications of the law of large numbers are not confined to the referred sectors and could be widely applied to industrial organizations and service provider companies in which large number of stochastic phenomena incorporate in their planning. In this thesis, the applications of the law of large numbers are studied in relation to logistics and transportation under conditions of operating in large networks. The results of this study assert that transportation companies can benefit from operating in large networks to increase the filling performance of their vehicles, fleet, etc. Equivalently, according to the law of large numbers the inferior capacity utilization in unit loads, containers, etc. converges to 0 with probability 1 as the size of the network grows. / Uppsatsnivå: D

Quase três séculos já se passaram desde que a primeira versão do chamado paradoxo de São Petersburgo chegou aos meios acadêmicos através do trabalho de Daniel Bernoulli. Contudo, a relevância desse assunto ainda reverbera em artigos científicos atuais em diversas áreas do conhecimento (notadamente, mas não exclusivamente, na Economia e na Estatística). Um jogo de enunciado simples cuja esperança matemática dos ganhos do jogador surpreendentemente é infinita, entretanto, dificilmente alguém estaria disposto a pagar qualquer taxa de entrada cobrada para jogá-lo. No presente trabalho buscou-se em primeiro lugar apresentar uma análise crítica do desenvolvimento histórico das \"soluções\" propostas para o paradoxo. Em seguida mostrou-se uma aplicação direta do paradoxo a um modelo matemático utilizado até hoje para avaliar o preço justo de ações. Por fim, revisaram-se alguns resultados obtidos pela moderna teoria da probabilidade através da convergência em probabilidade. / It has been almost three centuries since the first version of the so-called St. Petersburg Paradox has reached the academic environment through the work of Daniel Bernoulli. However, the relevance of this subject still reverberates in new scientific papers in many knowledge fields (especially, but not exclusively, in Economics and Statistics). A game with a simple rule in which the mathematical expectation of the player\'s gains is unexpectedly infinite but hardly someone would be willing to pay any asked entrance fee to play it. In this work we pursued at first to present a critical analysis on the historical development of the proposed \"solutions\" to the paradox. After that, we showed an application of the paradox to a mathematical model, that is still in use today, to obtain a fair price of a stock share. At last we reviewed some results given by the modern probability theory through the convergence in probability.

Mathematica is an extremely powerful and flexible symbolic computer algebra system that enables the user to deal with complicated algebraic tasks. It can also easily handle the numerical and graphical sides. One such task is the derivation of moment generating functions (MGF) and characteristic functions (CF), demonstrably effective tools to characterize a distribution. In this paper, we define some rules in Mathematica to help in computing the MGF and CF for linear combination of independent random variables. These commands utilizes pattern-matching code that enhances Mathematica's ability to simplify expressions involving the product of algebraic terms. This enhancement to Mathematica's functionality can be of particular benefit for MGF and CF. Applications of these rules to determine mean, variance and distribution are illustrated for various independent random variables.

Le but de cette thèse est de développer la théorie de Freidlin-Wentzell pour des modèles des épidémies, afin de prédire le temps mis par les perturbations aléatoires pour éteindre une situation endémique "stable". Tout d'abord nous proposons une nouvelle démonstration plus courte par rapport à celle établit récemment (sous une hypothèse un peu différente, mais satisfaite dans tous les exemples de modèles de maladie infectieuses que nous avons à l'esprit) par Kratz et Pardoux (2017) sur le principe de grandes déviations pour les modèles des épidémies. Ensuite nous établissons un principe de grandes déviations pour des EDS poissoniennes réfléchies au bord d'un ouvert suffisamment régulier. Nous établissons aussi un résultat concernant la zone du bord la plus probable par laquelle le processus solution de l'EDS de Poisson va sortir du domaine d'attraction d'un équilibre stable de sa loi des grands nombres limite. Nous terminons cette thèse par la présentation des méthodes "non standard aux différences finis", appropriés pour approcher numériquement les solutions de nos EDOs ainsi que par la résolution d'un problème de contrôle optimal qui permet d'avoir une bonne approximation du temps d'extinction d'un processus d'infection. / In this thesis, we develop the Freidlin-Wentzell theory for the "natural'' Poissonian random perturbations of the above ODE in Epidemic Dynamics (and similarly for models in Ecology or Population Dynamics), in order to predict the time taken by random perturbations to extinguish a "stable" endemic situation. We start by a shorter proof of a recent result of Kratz and Pardoux (under a somewhat different hypothesis which is satisfied in all the cases we have examined so far), which establishes the large deviations principle for epidemic models. Next, we establish the large deviations principle for reflected Poisonian SDE at the boundary of a sufficiently regular open set. Then, we establish the result for the most likely boundary area by which the process will exit the domain of attraction of a stable equilibrium of an ODE. We conclude this thesis with the presentation of the "non - standard finite difference" methods, suitable to approach numerically the solutions of our ODEs as well as the resolution of an optimal control problem which allows to have a good approximation of the time of extinction of an endemic situation.

This thesis covers a utility optimizing model designed and calibrated for agents of the Swedish economy. The main ingredient providing for this specific country is the modeling of the pension accumulation and pension benefits, which closely mimics the Swedish system. This characteristic is important since it measures one of the only two diversities between genders, that is, the income. The second characteristic is the survival probability. Except for these differences in national statistics, men and women are equal. The reminding model parameters are realistically set estimates from the surrounding economy. When using the model, firstly a baseline agent representing the entire labor force is under the microscope for evaluating the model itself. Next, one representative woman and one representative man from the private and public sectors respectively, composes a set of four samples for investigation of heterogeneity in optimality. The optimum level of consumption and risk-proportion of liquid wealth are solved by maximizing an Epstein-Zin utility function using the method of dynamic programming. The results suggests that both genders benefit from adapting the customized solutions to the problem.

Cette thèse fournit une analyse de la probabilité, avec une considération particulière du rôle que jouent les symétries et l’invariance dans son caractère objectif. La thèse défend un dualisme rationnel-physique. Nous développons une théorie de la probabilité épistémique ainsi qu’une théorie de la probabilité physique. La première concerne les degrés de croyance rationnels ; la seconde, la propension singulière peu fluctuante sur laquelle émergent les fréquences relatives stables. Du côté épistémique, nous défendons le bayésianisme objectif et ses règles d’attribution de probabilité, l’attribution invariante et la maximisation d’entropie. Nous généralisons également le bayésianisme orthodoxe et sa règle de changement de probabilité, la conditionnalisation, à la minimisation de la divergence Kullback-Leibler. Le bayésianisme orthodoxe généralisé est développé à partir d’une analyse générale de l’apprentissage, incluant la théorie AGM et la théorie de rang. L’analyse de l’opposition des deux bayésianismes culmine dans un pluralisme du bayésianisme combiné, instancié par une famille de révisions probabilistes qui répondent au problème de l’itération. Du côté physique, nous développons une explication de la fréquence relative à partir de l’approche par la loi des grands nombres. Nous répondons au dilemme de Gillies, selon lequel une théorie scientifique objective de la propension singulière et de long terme est impossible. Dans ce cadre, nous développons la méthode des fonctions arbitraires comme attribution de propension singulière peu fluctuante, et proposons une analyse détaillée de la mécanique statistique et du cas paradigmatique du lancer de pièce. / This thesis analyses the concept of probability and the role of symmetry and invariance in its objectif character. It defends a rational-physical dualism. I first develop a theory of epistemic probability, which addresses (the rational degrees of belief. I also develop a theory of physical probability, conceived as single case propensity on which stable frequencies emerge. Epistemically, I defend an objective Bayesianism and its rules of probability attribution, that is, the invariant prior attribution and maximizing entropy. I also generalize orthodox Bayesianism and its rule of probability change, conditionalization, to the minimization of the Kullback-Leibler divergence. Generalized orthodox Bayesianim is developed from a general investigation of learning, which includes the AGM theory and ranking theory. I resolve the opposition of the two Bayesianims through a pluralism of combined Bayesianism, instantiated by a family of probabilistic revisions which solve the iteration problem. Physically, I explain stable relative frequencies with the law of large numbers approach. I answer Gillies dilemma, according to which there is no scientific objective theory of propensity that is both single case and long term. I here develop the method of arbitrary functions as an attribution of relatively stable single case propensity and analyse in detail statistical mechanics and the paradigmatic coin toss.