Knowledge diffusion networks consist of individuals who exchange knowledge and knowledge flows connecting the individuals. By studying knowledge diffusion in a network perspective, it helps us understand how the connections between individuals affect the knowledge diffusion processes. Existing research on knowledge diffusion networks mostly adopts a uni-dimensional perspective, where all the individuals in the networks are assumed to be of the same type. It also assumes that there is only one type of knowledge flow in the network. This dissertation proposes a multi-dimensional perspective of knowledge diffusion networks and examines the patterns of knowledge diffusion with Exponential Random Graph Model (ERGM) based approaches. The objective of this dissertation is to propose a framework that effectively addresses the multi-dimensionality of knowledge diffusion networks, to enable researchers and practitioners to conceptualize the multi-dimensional knowledge diffusion networks in various domains, and to provide implications on how to stimulate and control the knowledge diffusion process. The dissertation consists of three essays, all of which examine the multi-dimensional knowledge diffusion networks in a specific context, but each focuses on a different aspect of knowledge diffusion. Chapter 2 focuses on how structural properties of networks affect various types of knowledge diffusion processes in the domain of commercial technology. The study uses ERGM to simultaneously model multiple types of knowledge flows and examine their interactions. The objective is to understand the impacts of network structures on knowledge diffusion processes. Chapter 3 focuses on examining the impact of individual attributes and the attributes of knowledge on knowledge diffusion in the context of scientific innovation. Based on social capital theory, the study also utilizes ERGM to examine how knowledge transfer and knowledge co-creation can be affected by the attributes of individual researchers and the attributes of scientific knowledge. Chapter 4 considers the dynamic aspect of knowledge diffusion and proposes a novel network model extending ERGM to identify dynamic patterns of knowledge diffusion in social media. In the proposed model, dynamic patterns in social media networks are modeled based on the nodal attributes of individuals and the temporal information of network ties.

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In this dissertation, we have proposed two new algorithms for statistical inference for models with intractable normalizing constants: the Monte Carlo Metropolis-Hastings algorithm and the Bayesian Stochastic Approximation Monte Carlo algorithm. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. At each iteration, it replaces the unknown normalizing constant ratio by a Monte Carlo estimate. Although the algorithm violates the detailed balance condition, it still converges, as shown in the paper, to the desired target distribution under mild conditions. The BSAMC algorithm works by simulating from a sequence of approximated distributions using the SAMC algorithm. A strong law of large numbers has been established for BSAMC estimators under mild conditions. One significant advantage of our algorithms over the auxiliary variable MCMC methods is that they avoid the requirement for perfect samples, and thus it can be applied to many models for which perfect sampling is not available or very expensive. In addition, although the normalizing constant approximation is also involved in BSAMC, BSAMC can perform very robustly to initial guesses of parameters due to the powerful ability of SAMC in sample space exploration. BSAMC has also provided a general framework for approximated Bayesian inference for the models for which the likelihood function is intractable: sampling from a sequence of approximated distributions with their average converging to the target distribution. With these two illustrated algorithms, we have demonstrated how the SAMCMC method can be applied to estimate the parameters of ERGMs, which is one of the typical examples of statistical models with intractable normalizing constants. We showed that the resulting estimate is consistent, asymptotically normal and asymptotically efficient. Compared to the MCMLE and SSA methods, a significant advantage of SAMCMC is that it overcomes the model degeneracy problem. The strength of SAMCMC comes from its varying truncation mechanism, which enables SAMCMC to avoid the model degeneracy problem through re-initialization. MCMLE and SSA do not possess the re-initialization mechanism, and tend to converge to a solution near the starting point, so they often fail for the models which suffer from the model degeneracy problem.

Bayesian principle is conceptually simple and intuitively plausible to carry out but its numerical implementation is not always straightforward. Most of the times we have posterior distributions in terms of complicated analytical funs ions and be known only up to a multiplicative constant. Hence it becomes computationally difficult to attain the marginal densities and the moments of the posterior distributions in closed form. In the present study the leading methods, both analytical and numerical, for implementing Bayesian inference has been explored. In particular, the non-iterative Monte Carlo method known as Importance Sampling has been applied to approximate the posterior expectations of the Lognormal and Cauchy distributions, belonging to the Exponential family and the non-Exponential family of distributions respectively. Sample values from these distributions have been simulated through computer programming. Calculations are done mostly by C++ programming language and Mathematica. / Department of Mathematical Sciences

The major part of this thesis has been devoted to path and motion generation synthesis of spatial mechanisms. For the first time kinematic synthesis methods have been developed based on the algebra of exponential rotation matrices. Besides modeling spatial pairs such as spheric, cylindric and Hook&#039 / s joints by combinations of revolute and prismatic joints and applying Denavit-Hartenberg&#039 / s convention, general loop closure equations have been presented for path and motion generation synthesis of any spatial mechanism with lower kinematic pairs. In comparison to the existing synthesis methods the main advantage of the methods presented in this thesis is that, general loop closure equations have been presented for any kind of spatial mechanism with lower kinematic pairs. Besides these methods enable the designer to benefit the advantages of the algebra of exponential rotation matrices. In order to verify the applicability of the synthesis methods presented in this thesis, the general loop closure equations of RSHR, RCCR and RSSR-SC mechanisms have been determined and then using these equations six numerical examples have been solved. Some tables have been presented based on the determined loop closure equations which reveal useful information about the number of precision points or positions that can be considered for the kinematic synthesis of the above mentioned mechanisms and the number of free parameters. In numerical examples, the mechanisms have been synthesized based on the general loop closure equations and the synthesis algorithms presented in the thesis. Although in some cases semi-analytical solutions have been obtained, in most of the cases, the loop closure equations were solved by computer programs written by Mathcad. The input angle-output angle diagrams drawn at the end of each numerical example illustrate the motion continuity of the mechanisms and that branching has been avoided. Detailed information has been given about the computer programs and the difficulties which may arise while synthesizing spatial mechanisms. In addition to the above mentioned points, a mobility analysis has been done for the RCCR mechanism and some inequalities have been obtained in terms of the link lengths. The swing angle diagram of the RCCR linkage has been drawn too.

This thesis deals with the bootstrap method. Three decades after the seminal paper by Bradly Efron, still the horizons of this method need more exploration. The research presented herein has stepped into different fields of statistics where the bootstrap method can be utilized as a fundamental statistical tool in almost any application. The thesis considers various statistical problems, which is explained briefly below. Bootstrap method: A comparison of the parametric and the nonparametric bootstrap of variance is presented. The bootstrap of ranked set sampling is dealt with, as well as the wealth of theories and applications on the RSS bootstrap that exist nowadays. Moreover, the performance of RSS in resampling is explored. Furthermore, the application of the bootstrap method in the inference of contingency table test is studied. Coefficient of variation: This part shows the capacity of the bootstrap for inferring the coefficient of variation, a task which the asymptotic method does not perform very well. Information theory: There are few works on the study of information theory, especially on the inference of entropy. The papers included in this thesis try to achieve the inference of entropy using the bootstrap method.

We develop well-posedness theory and analytical and numerical solution techniques for Boussinesq-type equations. Firstly, we consider the Cauchy problem for a generalized Boussinesq equation. We show that under suitable conditions, a global solution for this problem exists. In addition, we derive sufficient conditions for solution blow-up in finite time. / Secondly, a generalized Jacobi/exponential expansion method for finding exact solutions of non-linear partial differential equations is discussed. We use the proposed expansion method to construct many new, previously undiscovered exact solutions for the Boussinesq and modified Korteweg-de Vries equations. We also apply it to the shallow water long wave approximate equations. New solutions are deduced for this system of partial differential equations. / Finally, we develop and validate a numerical procedure for solving a class of initial boundary value problems for the improved Boussinesq equation. The finite element method with linear B-spline basis functions is used to discretize the equation in space and derive a second order system involving only ordinary derivatives. It is shown that the coefficient matrix for the second order term in this system is invertible. Consequently, for the first time, the initial boundary value problem can be reduced to an explicit initial value problem, which can be solved using many accurate numerical methods. Various examples are presented to validate this technique and demonstrate its capacity to simulate wave splitting, wave interaction and blow-up behavior.

Η παρούσα μεταπτυχιακή διατριβή εντάσσεται ερευνητικά στην περιοχή της Στατιστικής Θεωρίας Αποφάσεων και ειδικότερα στην εκτίμηση των παραμέτρων στο μοντέλο της διπαραμετρικής εκθετικής κατανομής με παράμετρο θέσης μ και παράμετρο κλίμακος σ. Θεωρούμε ένα δείγμα n τυχαίων μεταβλητών, καθεμία από τις οποίες ακολουθεί την διπαραμετρική εκθετική κατανομή. Λογοκρίνουμε κάποιες αρχικές παρατηρήσεις και έστω ότι τερματίζουμε το πείραμά μας πριν αποτύχουν όλες οι συνιστώσες. Τότε προκύπτει ένα διπλά διακεκομμένο δείγμα διατεταγμένων παρατηρήσεων. Η εκτίμηση των παραμέτρων της διπαραμετρικής εκθετικής κατανομής, γίνεται από το συγκεκριμένο δείγμα. Πρώτα μελετάμε κάποιες βασικές έννοιες της Στατιστικής και της Εκτιμητικής και βρίσκουμε εκτιμητές για τις παραμέτρους. Πιο συγκεκριμένα, βρίσκουμε αμερόληπτο εκτιμητή ελάχιστης διασποράς, εκτιμητή μέγιστης πιθανοφάνειας, εκτιμητή με την μέθοδο των ροπών και τον βέλτιστο αναλλοίωτο εκτιμητή σε συγκεκριμένη κλάση, αντίστοιχα και για τις δύο παραμέτρους. Σαν βελτίωση των προηγούμενων εκτιμητών, ακολουθούν οι εκτιμητές τύπου Stein και, ολοκληρώνοντας, ασχολούμαστε με πρόβλεψη κατά Bayes για μια μελλοντική παρατήρηση / The present master thesis deals with the estimation of the location parameter μ and the scale parameter σ of the two-parameter exponential distribution. A sample n of random variables from the two-parameter exponential distribution is assumed. Part of the initial variables is censored and the experiment is terminated before all the components fail. A doubly censored sample emerges from which the two-parameter exponential distribution's parameters are estimated. First of all, basic Statistics' concepts are studied in order to estimate the parameters. More specifically, the Minimum Variance Unbiased Estimator (MVUE), the Maximum Likelihood Estimator (MLE), the estimator based on the Method of Moments and the best affine equivariant estimator are computed for both the parameters. To improve the previous estimators, the Stein method is used and to conclude the Bayes prediction is used for future observation

CAPES / Ao longo da realização deste trabalho buscamos compreender melhor as funções exponenciais e logarítmicas de modo que pudéssemos apresentá-las de maneira diferente da abordagem tradicional. Em um primeiro momento resgatamos os conceitos de potenciação, desde os expoentes naturais, passando pelos expoentes inteiros e racionais, e chegando aos expoentes reais e depois definindo o logaritmo como “operação inversa” da potenciação. Em seguida caracterizamos a função exponencial através de propriedades básicas (ser monótona e levar somas em produtos) e definimos o logaritmo como sua função inversa. Depois disso, fizemos o mesmo com a função logarítmica, definindo-a através de propriedades básicas (ser crescente e levar produtos em somas) para então definir a função exponencial como sua inversa, mostrando por fim, que ambas as formas de definir as funções exponenciais, e consequentemente as logarítmicas, são equivalentes. Por fim, trazemos uma caracterização geométrica dos logaritmos, tornando as demonstrações das propriedades mais intuitivas e simples. / Along this work we search to better understand the exponential and logarithmic functions so that we could present them differently from the traditional approach. In a first moment we recovered the concepts of potentiation, from the natural exponents, through the entire rational exponents, to the real exponents and then defining the logarithm as the ”reverse operation”of potentiation. Then we characterize the exponential function through basic properties (be monotonous and take sums into products) and define the logarithm as its inverse function. After that, we did the same with the logarithmic function, defining it through basic properties (being increasing and taking products into sums) and then defining the exponential function as its inverse, showing, finally, that both ways of defining the exponential functions and, consequently, the logarithmic functions, are equivalent. Finally, we bring a geometric characterization of the logarithms, making the demonstrations of properties more intuitive and simple.

This thesis studies numerical methods for integrating the master equations describing Markov chain models of cardiac ion channels. Such models describe the time evolution of the probability that ion channels are in a particular state. Numerical simulations of such models are often computationally demanding because many solvers require relatively small time steps to ensure numerical stability. The aim of this project is to analyse selected Markov chains and develop more efficient and accurate solvers. We separate a Markov chain model into fast and slow time-scales based on the speed of transitions between states. Eliminating the fast transitions, we find an asymptotic reduction of zeroth-order and first-order in a small parameter describing the time-scales separation. We apply the theory to a Markov chain model of the fast sodium channel INa. We consider several variants for classifying some transitions as fast in order to find reduced systems that yield a good accuracy. However, the time step size is still restricted by numerical instabilities. We adapt the Rush-Larsen technique originally developed for gate models. Assuming that a transition matrix can be considered constant during each time step, we solve the Markov chain model analytically. The solution provides a recipe for a stable exponential solver, which we call "Matrix Rush-Larsen" (MRL). Using operator splitting we design an even more flexible "hybrid" method that combines the MRL with other solvers. The resulting improvement in stability allows a large increase in the time step size. In some models, we obtain reasonably accurate results 27 times faster using a hybrid method than with the forward Euler method, even with the maximal time step allowed by the stability constraint. Finally, we extend the cardiac simulation package BeatBox by the developed exponential solvers. We upgrade a format of "ionic" modules which describe a cardiac cell, in order to allow for a specific definition of Markov chain models. We also modify a particular integrator for ionic modules to include the MRL and the hybrid method. To test the functionality of the code, we have converted a number of cellular models into the ionic format. The documented code is available in the official BeatBox package distribution.