Probabilistic machine learning and circular statistics—the branch of statistics concerned with data as angles and directions—are two research communities that have grown mostly in isolation from one another. On the one hand, probabilistic machine learning community has developed powerful frameworks for problems whose data lives on Euclidean spaces, such as Gaussian Processes, but have generally neglected other topologies studied by circular statistics. On the other hand, the approximate inference frameworks from probabilistic machine learning have only recently started to the circular statistics landscape. This thesis intends to redress the gap between these two fields by contributing to both fields with models and approximate inference algorithms. In particular, we introduce the multivariate Generalised von Mises distribution (mGvM), which allows the use of kernels in circular statistics akin to Gaussian Processes, and an augmented representation. These models account for a vast number of applications comprising both latent variable modelling and regression of circular data. Then, we propose methods to conduct approximate inference on these models. In particular, we investigate the use of Variational Inference, Expectation Propagation and Markov chain Monte Carlo methods. The variational inference route taken was a mean field approach to efficiently leverage the mGvM tractable conditionals and create a baseline for comparison with other methods. Then, an Expectation Propagation approach is presented drawing on the Expectation Consistent Framework for Ising models and connecting the approximations used to the augmented model presented. In the final MCMC chapter, efficient Gibbs and Hamiltonian Monte Carlo samplers are derived for the mGvM and the augmented model.

Analysis of circular data becomes more and more popular in many fields of studies. In this report, I present two statistical analysis of circular data using von Mises distributions. Firstly, the maximization-expectation algorithm is reviewed and used to classify and estimate circular data from the mixture of von Mises distributions. Secondly, Forward Filtering Backward Smoothing method via particle filtering is reviewed and implemented when circular data appears in the dynamic state-space models. / text

Six new structural parameters £rB, £pB, £rC, £pC, £rS, and £pS are proposed to enhance the side chain actions in protein structures. Programs for calculating these new parameters based on phi and psi torsion angles vector algebra calculation method are established. A bivariate model with von Mises marginal distributions are applied to establish models of phi and psi in protein class Ophiophagus hannah neurotoxins and alpha-bungarotoxins respectively. 11 global structural parameters include phi and psi torsion angles, bond lengths of C-N, C-O, C£\ -C, and N-C£\, and bond angles of C-N-C£\, C£\-C-N, C£\-C-O, N-C£\-C, and O-C-N are considered to classify long alpha-neurotoxins by Ward's cluster method and LIBSVM program package. Those global structural parameters of loop II Trp residues of alpha-neurotoxins are discussed.

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The aim of this thesis is to analyse literature with focusing on literature about directional distribution of collagen fibres. This knowledge is very important for computational modelling and FEM analysis of arterial wall. Comparison of suitability of different models of directional distribution of collagen fibres is made by fitting of different types of probability density functions. Impact assessment of different collagen fibres distribution on mechanical properties of the arterial wall and impact assessment of wall anisotropy is solved with finite element method. FEM analysis is done on three loading types – uniaxial tension, equibiaxial tension and inflation of artery by internal pressure. Output of this thesis is evaluation of results for various types of collagen fibres arrangement in arterial wall.

Η εκπόνηση της συγκεκριμένης Μεταπτυχιακής Εργασίας, εξετάζει, καταρχήν, την έννοια της πιθανότητας και τις βασικές ιδιότητές της, όπως την τυχαία μεταβλητή και τη συνάρτηση κατανομής. Παράλληλα όμως, παρουσιάζει στοιχεία βασικών διακριτών και συνεχών κατανομών, όπως της κανονικής, της ομοιόμορφης, της Poisson, και άλλων κατανομών της γραμμικής στατιστικής. Στη συνέχεια, αναφέρεται στις βασικές έννοιες της περιγραφικής στατιστικής, όπως οργάνωση και γραφική αναπαράσταση στατιστικών δεδομένων, ομαδοποίηση παρατηρήσεων, ιστόγραμμα συχνοτήτων, καθώς και περιγραφικά μέτρα γραμμικών δεδομένων. Κυρίως, όμως, η παρούσα μελέτη αποτελεί μία γενική επισκόπηση των στατιστικών μεθόδων παρουσίασης και ανάλυσης των περιοδικών δεδομένων. Με τον όρο "περιοδικά δεδομένα", εννοούμε τυχαίες διευθύνσεις και κατευθύνσεις προσανατολισμού. Η παρουσίασης των τυχαίων γωνιών, των γραφικών αναπαραστάσεων των περιοδικών δεδομένων καθώς και των περιγραφικών μέτρων - μέτρα θέσεως, διασποράς, λοξότητας, κυρτώσεως - θα μας οδηγήσουν σε μία καλύτερη προσέγγιση, κατανόηση των περιοδικών κατανομών. Επιπλέον, θα παρουσιαστούν αναλυτικά οι βασικές περιοδικές κατανομές, ομοιόμορφη και Von Mises κατανομή. Όμως, θα εξεταστούν και άλλες κατανομές μονοκόρυφες ή πολυκόρυφες, όπως οι περιελιγμένες κατανομές , η συνημίτονο και η καρδιοειδής κατανομή, οι λοξές κατανομές κ.ά. Τέλος, η εργασία θα αναφερθεί σε μία οικογένεια συμμετρικών περιοδικών κατανομών που προτάθηκε από τον κύριο Παπακωνσταντίνου και αποτελεί επέκταση της καρδιοειδούς κατανομής,σύμφωνα με εργασία των επιστημόνων Toshihiro Abe,Arthur Pewsey,Kunio Shimizu, παρέχοντας σημαντικά πλεονεκτήματα σε σχέση με άλλες οικογένειες κατανομών. / The preparation of this thesis examines, in principle,the concept of probability and its basic properties, such as the random variable and distribution function and presents data of basic discrete and continuous distributions, including normal, uniform, the Poisson, and other distributions of linear statistics. Then it refers to the basic concepts of descriptive statistics, such as the organization and the graphic representation of statistical data, grouping observations Frequency histogram as well as descriptive measures of linear data. Mostly, though, this study represents an overview of statistic methods of presentation and analysis of periodic data. By the term "periodic data" we mean random addresses and directions orientation. The presentation of random angles, graphic representations of periodic data and descriptive measures - measures of location, dispersion, skewness and kurtosis - will lead us to a better approach and understanding of periodic distributions. Furthermore, we present in detail the basic periodic distributions, the uniform and the Von Mises distribution. But other unimodal and multimodal distributions will be examined such as wrapped distributions, the cosine and cardioid distribution, skewed distributions, etc. Finally, this thesis will mention a family of symmetric periodic distributions proposed by Mr. Papakonstantinou and an extension of the cardioid distribution, according to the paper published by the scientists Toshihiro Abe,Arthur Pewsey and Kunio Shimizu, where significant advantages are provided over other families of distributions.

In this thesis, we present some new results in distribution theory for both discrete and continuous random variables, together with their motivating applications. We start with some results about the Multivariate Gaussian Distribution and its characterization as a maximizer of the Strichartz Estimates. Then, we present some characterizations of discrete and continuous distributions through ideas coming from optimal transportation. After this, we pass to the Simpson's Paradox and see that it is ubiquitous and it appears in Quantum Mechanics as well. We conclude with a group of results about discrete and continuous distributions invariant under symmetries, in particular invariant under the groups $A_1$, an elliptical version of $O(n)$ and $\mathbb{T}^n$. As mentioned, all the results proved in this thesis are motivated by their applications in different research areas. The applications will be thoroughly discussed. We have tried to keep each chapter self-contained and recalled results from other chapters when needed. The following is a more precise summary of the results discussed in each chapter. In chapter \ref{chapter 2}, we discuss a variational characterization of the Multivariate Normal distribution (MVN) as a maximizer of the Strichartz Estimates. Strichartz Estimates appear as a fundamental tool in the proof of wellposedness results for dispersive PDEs. With respect to the characterization of the MVN distribution as a maximizer of the entropy functional, the characterization as a maximizer of the Strichartz Estimate does not require the constraint of fixed variance. In this chapter, we compute the precise optimal constant for the whole range of Strichartz admissible exponents, discuss the connection of this problem to Restriction Theorems in Fourier analysis and give some statistical properties of the family of Gaussian Distributions which maximize the Strichartz estimates, such as Fisher Information, Index of Dispersion and Stochastic Ordering. We conclude this chapter presenting an optimization algorithm to compute numerically the maximizers. Chapter \ref{chapter 3} is devoted to the characterization of distributions by means of techniques from Optimal Transportation and the Monge-Amp\`{e}re equation. We give emphasis to methods to do statistical inference for distributions that do not possess good regularity, decay or integrability properties. For example, distributions which do not admit a finite expected value, such as the Cauchy distribution. The main tool used here is a modified version of the characteristic function (a particular case of the Fourier Transform). An important motivation to develop these tools come from Big Data analysis and in particular the Consensus Monte Carlo Algorithm. In chapter \ref{chapter 4}, we study the \emph{Simpson's Paradox}. The \emph{Simpson's Paradox} is the phenomenon that appears in some datasets, where subgroups with a common trend (say, all negative trend) show the reverse trend when they are aggregated (say, positive trend). Even if this issue has an elementary mathematical explanation, the statistical implications are deep. Basic examples appear in arithmetic, geometry, linear algebra, statistics, game theory, sociology (e.g. gender bias in the graduate school admission process) and so on and so forth. In our new results, we prove the occurrence of the \emph{Simpson's Paradox} in Quantum Mechanics. In particular, we prove that the \emph{Simpson's Paradox} occurs for solutions of the \emph{Quantum Harmonic Oscillator} both in the stationary case and in the non-stationary case. We prove that the phenomenon is not isolated and that it appears (asymptotically) in the context of the \emph{Nonlinear Schr\"{o}dinger Equation} as well. The likelihood of the \emph{Simpson's Paradox} in Quantum Mechanics and the physical implications are also discussed. Chapter \ref{chapter 5} contains some new results about distributions with symmetries. We first discuss a result on symmetric order statistics. We prove that the symmetry of any of the order statistics is equivalent to the symmetry of the underlying distribution. Then, we characterize elliptical distributions through group invariance and give some properties. Finally, we study geometric probability distributions on the torus with applications to molecular biology. In particular, we introduce a new family of distributions generated through stereographic projection, give several properties of them and compare them with the Von-Mises distribution and its multivariate extensions. / Thesis / Doctor of Philosophy (PhD)