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.

Reziumė Disertacijoje nagrinėjamos atsitiktinių keitinių problemos yra priskirtinos tikimybinei kombinatorikai. Gauti rezultatai aprašo visiškai adityviųjų funkcijų, apibrėžtų simetrinėje grupėje, reikšmių asimptotinius skirstinius Evenso tikimybinio mato atžvilgiu, kai grupės eilė neaprėžtai didėja. Išvestos adityviųjų funkcijų laipsninių ir faktorialinių momentų formulės. Funkcijų, išreiškiančių atsitiktinio keitinio ciklų su bet kokiais apribojimais skaičius, atveju rastos būtinos ir pakankamos ribinių tikimybinių dėsnių egzistavimo sąlygos. Išsamiai išnagrinėtas konvergavimas į Puasono, quasi-Puasono, Bernulio, binominio ir kitus skirstinius, sukoncentruotus sveikųjų neneigiamų skaičių aibėje. Rezultatai apibendrinti sveikareikšmių visiškai adityviųjų funkcijų klasėje. Darbe įrodytas bendras silpnasis didžiųjų skaičių dėsnis, rastos būtinos ir pakankamos adityviųjų funkcijų sekų pasiskirstymo funkcijų konvergavimo į išsigimusį nuliniame taške dėsnį egzistavimo sąlygos. Sprendžiamos problemos yra susietos su tikimybiniais vektorių, turinčių sveikąsias neneigiamas koordinates, uždaviniais. Adicinėje tokių vektorių pusgrupėje išnagrinėti multiplikatyviųjų funkcijų vidurkiai tikimybinio mato, vadinamo Evanso atrankos formule, atžvilgiu. Gauti tikslūs viršutinieji ir apatinieji įverčiai. Iš jų išplaukia svarbios atsitiktinių keitinių tikimybių savybės. Disertacijoje plėtojami faktorialinių momentų ir kiti kombinatoriniai bei tikimybiniai metodai. / In the thesis the examining problems of random permutations are attributed to the probabilistic combinatorics. Obtained results describe asymptotical distributions of completely additive functions values defined on a symmetric group with respect to Ewens probability measure, if the group order unbounded increases. Power and factorial moments formulae of additive functions are derived. There are established necessary and sufficient conditions under which the distributions of a number of cycles with restricted lengths obey the limit probability laws. The convergence to the Poisson, quasi-Poisson, Bernoulli, binomial and other distributions, defined on the positive whole - number set are exhaustively investigated. The results are generalized on the class of whole - number completely additive functions. The general weak law of large numbers is proved in the thesis, necessary and sufficient existence conditions, under which the distributions of the sequences of additive functions converge to the degenerate at the point zero limit law are established. Examining problems are related to the probability tasks of the vectors, which have whole - numbered nonnegative coordinates. The mean values of multiplicative functions defined on those vectors’ additive semigroup with respect to the Ewens measure, called Ewens Sampling Formula, and investigated. Lower and upper sharp estimates are obtained. From the latter results follow important probabilities’ properties of random... [to full text]

<p>This dissertation establishes limit theory for topological functionals of geometric complexes from a stochastic process viewpoint. Standard filtrations of geometric complexes, such as the Čech and Vietoris-Rips complexes, have a natural parameter <i>r </i>which governs the formation of simplices: this is the basis for persistent homology. However, the parameter <i>r</i> may also be considered the time parameter of an appropriate stochastic process which summarizes the evolution of the filtration.</p><p>Here we examine the stochastic behavior of two of the foremost classes of topological functionals of such filtrations: the Betti numbers and the Euler characteristic. There are also two distinct setups in which the points underlying the complexes are generated, where the points are distributed randomly in <i>R^d</i> according to a general density (the traditional setup) and where the points lie in the tail of a heavy-tailed or exponentially-decaying “noise” distribution (the extreme-value theory (EVT) setup).<br></p><p>These results constitute some of the first results combining topological data analysis (TDA) and stochastic process theory. The first collection of results establishes stochastic process limits for Betti numbers of Čech complexes of Poisson and binomial point processes for two specific regimes in the traditional setup: the sparse regime—when the parameter <i>r </i>governing the formation of simplices causes the Betti numbers to concentrate on components of the lowest order; and the critical regime—when the parameter <i>r</i> is of the order <i>n^-1/d</i> and the geometric complex becomes highly connected with topological holes of every dimension. The second collection of results establishes a functional strong law of large numbers and a functional central limit theorem for the Euler characteristic of a random geometric complex for the critical regime in the traditional setup. The final collection of results establishes functional strong laws of large numbers for geometric complexes in the EVT setup for the two classes of “noise” densities mentioned above.<br></p>

<p>This dissertation establishes a variety of limit theorems pertaining to rare events in stochastic topology, exploiting probabilistic methods to study simplicial complex models. We focus on the filtration of \vc ech complexes and examine the asymptotic behavior of two topological functionals: the Betti numbers and critical faces. The filtration involves a parameter rn>0 that determines the growth rate of underlying Cech complexes. If rn depends also on the time parameter t, the obtained limit theorems will be established in a functional sense.</p> <p>The first part of this dissertation is devoted to investigating the layered structure of topological complexity in the tail of a probability distribution. We establish the functional strong law of large numbers for Betti numbers, a basic quantifier of algebraic topology, of a geometric complex outside an open ball of radius Rn, such that Rn to infinity as the sample size n increases. The nature of the obtained law of large numbers is determined by the decay rate of a probability density. It especially depends on whether the tail of a density decays at a regularly varying rate or an exponentially decaying rate. The nature of the limit theorem depends also on how rapidly Rn diverges. In particular, if Rn diverges sufficiently slowly, the limiting function in the law of large numbers is crucially affected by the emergence of arbitrarily large connected components supporting topological cycles in the limit.</p> <p>The second part of this dissertation investigates convergence of point processes associated with critical faces for a Cech filtration built over a homogeneous Poisson point process in the d-dimensional flat torus. The convergence of our point process is established in terms of the  Mo-topology, when the connecting radius of a Cech complex decays to 0, so slowly that critical faces are even less likely to occur than those in the regime of threshold for homological connectivity. We also obtain a series of limit theorems for positive and negative critical faces, all of which are considerably analogous to those for critical faces.</p>

Voir la bibliographie du mémoire pour les références du résumé. See the thesis`s bibliography for the references in the summary. / Ce mémoire étudie le comportement du maximum et des hauts points de la marche aléatoire branchante et du champ libre gaussien discret en dimension deux lorsque la variance de leurs accroissements est inhomogène dans le temps. Nous regardons le cas où il y a un nombre fini d'échelles $0 = \lambda_0 < \lambda_1 < ... < \lambda_M = 1$ et des paramètres de variance $\sigma_i > 0$ associés aux intervalles de temps $[\lambda_{i-1},\lambda_i]$. La marche aléatoire branchante inhomogène généralise le modèle considéré dans [23] et le champ libre gaussien inhomogène généralise le modèle introduit dans [4]. Le but du mémoire est d'étendre les résultats connus sur la convergence du maximum [5,6,23] et le nombre de hauts points [16] à ces deux nouveaux champs gaussiens. Les résultats aident à mieux comprendre comment la perturbation des corrélations dans l'un ou l'autre des modèles de base influence l'ordre de grandeur du maximum et l'ordre du nombre de hauts points. / This thesis studies the behavior of the maximum and high points of the branching random walk and the Gaussian free field when the variance of their increments is time-inhomogeneous. We look at the case where there are a finite number of scales $0 = \lambda_0 < \lambda_1 < ... < \lambda_M = 1$ and variance parameters $\sigma_i > 0$ associated with the time intervals $[\lambda_{i-1},\lambda_i]$. The inhomogeneous branching random walk generalizes the model considered in [23] and the inhomogeneous Gaussian free field generalizes the model introduced in [4]. The purpose of the thesis is to extend known results on the convergence of the maximum [5,6,23] and the number of high points [16] to these new Gaussian fields. The results help to better understand how perturbations of the correlations in one or the other basic models influence the order of magnitude of the maximum and the order of the number of high points.

Im ersten Teil der Dissertation wird ein diskretes stochastisches zustandsabhängiges Modell eines zweiseitigen Limit Orderbuchs als bestehend aus den Zustandsgrößen bester Bidpreis (Geldkurs), bester Askpreis (Briefkurs) und vorhandener Kauf- bzw. Verkaufsdichte definiert. Für eine einfache Skalierung mit zwei Zeitskalen wird ein Grenzwertsatz bewiesen. Die Veränderungen der besten Bid- und Askpreise werden im Sinne des Gesetzes der großen Zahlen skaliert und dies entspricht der langsameren Zeitskala. Das Platzieren bzw. Stornieren der Limitorder findet auf der schnelleren Zeitskala statt. Der Grenzwertsatz besagt, dass die fundamentalen Zustandsgrößen, gegeben Regularitätsbedingungen der einkommenden Order, fast sicher zu einem stetigen Limesmodell konvergieren. Im Limesmodell sind der beste Bidpreis und der beste Askpreis die eindeutigen Lösungen von zwei gekoppelten gewöhnlichen DGLen. Die Kauf- und Verkaufsdichten sind jeweils als eindeutige Lösungen von linearen hyperbolischen PDGLen, die anhand der Erwartungswerte der einkommenden Orderparameter festgelegt sind, gegeben. Die Lösungen sind in geschlossener Form erhältlich. Im zweiten Teil wird ein funktionaler zentraler Grenzwertsatz d.h. ein Invarianzprinzip für ein vereinfachtes Modell eines Limitorderbuches bewiesen. Unter einer natürlichen Skalierung konvergiert der zweidimensionale Preisprozess (Bid- und Askpreis) in Verteilung zu einer Semimartingal reflektierten Brownschen Bewegung in der zugelassenen Preismenge. Gleichzeitig konvergieren die Kauf- und Verkaufsdichten im schwachen Sinn zum Betrag einer zweiparametrischen Brownschen Bewegung. Es wird weiterhin anhand eines Beispiels gezeigt, wie man für das Modell im ersten Teil eine stochastiche PDGL, unter einer starken Stationaritätsannahme für die Orderplatzierungen und -stornierungen, herleiten kann. Im dritten Teil wird ein Mittelungs- bzw. ein Invarianzprinzip für diskrete Banach- bzw. Hilbertraumwertige stochastische Prozesse bewiesen. / In the first part of the thesis, we define a random state-dependent discrete model of a two-sided limit order book in terms of its key quantities best bid [ask] price and the standing buy [sell] volume density. For a simple scaling that introduces a slow time scaling, that is equivalent to the classical law of large numbers, for the bid/ask prices and a faster time scale for the limit volume placements/cancelations, that keeps the expected volume rate over the considered price interval invariant, we prove a limit theorem. The limit theorem states that, given regularity conditions on the random order flow, the key quantities converge in the sense of a strong law of large numbers to a tractable continuous limiting model. The limiting model is such that the best bid and ask price dynamics can be described in terms of two coupled ODE:s, while the dynamics of the relative buy and sell volume density functions are given as the unique solutions of two linear first-order hyperbolic PDE:s with variable coefficients, specified by the expectation of the order flow parameters. In the second part, we prove a functional central limit theorem i.e. an invariance principle for an order book model with block shaped volume densities close to the spread. The weak limit of the two-dimensional price process (best bid and ask price) is given by a semi-martingale reflecting Brownian motion in the set of admissible prices. Simultaneously, the relative buy and sell volume densities close to the spread converge weakly to the modulus of a two-parameter Brownian motion. We also demonstrate an example how to easily derive an SPDE for the relative volume densities in a simple case, when a strong stationarity assumption is made on the limit order placements and cancelations for the model suggested in the first part. In the third and final part of the thesis, we prove an averaging and an invariance principle for discrete processes taking values in Banach and Hilbert spaces, respectively.

This report focus upon implied volatility for American styled Asian options, and a least squares approximation method as a way of estimating its magnitude. Asian option prices are calculated/approximated based on Quasi-Monte Carlo simulations and least squares regression, where a known volatility is being used as input. A regression tree then empirically builds a database of regression vectors for the implied volatility based on the simulated output of option prices. The mean squared errors between imputed and estimated volatilities are then compared using a five-folded cross-validation test as well as the non-parametric Kruskal-Wallis hypothesis test of equal distributions. The study results in a proposed semi-parametric model for estimating implied volatilities from options. The user must however be aware of that this model may suffer from bias in estimation, and should thereby be used with caution.

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Generating functions express a sequence as coefficients arising from a power series in variables. They have many applications in combinatorics and probability. In this paper, we will investigate the important properties of four kinds of generating functions in one variables: ordinary generating unction, exponential generating function, probability generating function and moment generating function. Many examples with applications in combinatorics and probability, will be discussed. Finally, some well-known contest problems related to generating functions will be addressed.

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