<p>Queueing models have been used extensively in the design of call centres. In particular, a queueing model will be used to describe a help desk which is a form of a call centre. The design of the queueing model involves modelling the arrival an service processes of the system.</p><p>Conventionally, the arrival process is assumed to be Poisson and service times are assumed to be exponentially distributed. But it has been proposed that practically these are seldom the case. Past research reveals that the log-normal distribution can be used to model the service times in call centres. Also, services may involve stages/tasks before completion. This motivates the use of a phase-type distribution to model the underlying stages of service.</p><p>This research work focuses on developing statistical models for the overall service times and the service times by job types in a particular help desk. The assumption of exponential service times was investigated and a log-normal distribution was fitted to service times of this help desk. Each stage of the service in this help desk was modelled as a phase in the phase-type distribution.</p><p>Results from the analysis carried out in this work confirmed the irrelevance of the assumption of exponential service times to this help desk and it was apparent that log-normal distributions provided a reasonable fit to the service times. A phase-type distribution with three phases fitted the overall service times and the service times of administrative and miscellaneous jobs very well. For the service times of e-mail and network jobs, a phase-type distribution with two phases served as a good model.</p><p>Finally, log-normal models of service times in this help desk were approximated using an order three phase-type distribution.</p>

<p>Queueing models have been used extensively in the design of call centres. In particular, a queueing model will be used to describe a help desk which is a form of a call centre. The design of the queueing model involves modelling the arrival an service processes of the system.</p><p>Conventionally, the arrival process is assumed to be Poisson and service times are assumed to be exponentially distributed. But it has been proposed that practically these are seldom the case. Past research reveals that the log-normal distribution can be used to model the service times in call centres. Also, services may involve stages/tasks before completion. This motivates the use of a phase-type distribution to model the underlying stages of service.</p><p>This research work focuses on developing statistical models for the overall service times and the service times by job types in a particular help desk. The assumption of exponential service times was investigated and a log-normal distribution was fitted to service times of this help desk. Each stage of the service in this help desk was modelled as a phase in the phase-type distribution.</p><p>Results from the analysis carried out in this work confirmed the irrelevance of the assumption of exponential service times to this help desk and it was apparent that log-normal distributions provided a reasonable fit to the service times. A phase-type distribution with three phases fitted the overall service times and the service times of administrative and miscellaneous jobs very well. For the service times of e-mail and network jobs, a phase-type distribution with two phases served as a good model.</p><p>Finally, log-normal models of service times in this help desk were approximated using an order three phase-type distribution.</p>

Sequential data are encountered in many contexts of everyday life and in numerous scientific applications. They can for instance be SMS typeset on mobile phones, web pages reached while crossing hyperlinks, system logs or DNA samples, to name a few. Generating such data defines a sequential process. This thesis is concerned with the modeling of sequential processes from observed data. Sequential processes are here modeled using probabilistic models, namely discrete time Markov chains (MC), Hidden Markov Models (HMMs) and Partially Observable Markov Models (POMMs). Such models can answer questions such as (i) Which event will occur a couple of steps later? (ii) How many times will a particular event occur? and (iii) When does an event occur for the first time given the current situation? The last question is of particular interest in this thesis and is mathematically formalized under the notion of First Passage Times (FPT) dynamics of a process. The FPT dynamics is used here to solve the three following problems related to machine learning and data mining: (i) HMM/POMM induction, (ii) supervised sequence classification and (iii) relevant subgraph mining. Firstly, we propose a novel algorithm, called POMMStruct, for learning the structure and the parameters of POMMs to best fit the empirical FPT dynamics observed in the samples. Experimental results illustrate the benefit of POMMStruct in the modeling of sequential processes with a complex temporal dynamics while compared to classical induction approaches. Our second contribution is concerned with the classification of sequences. We propose to model the FPT in sequences with discrete phase-type (PH) distributions using a novel algorithm called PHit. These distributions are used to devise a new string kernel and a probabilistic classifier. Experimental results on biological data shows that our methods provides state-of-the-art classification results. Finally, we address the problem of mining subgraphs, which are relevant to model the relationships between selected nodes of interest, in large graphs such as biological networks. A new relevance criterion based on particular random walks called K-walks is proposed as well as efficient algorithms to compute this criterion. Experiments on the KEGG metabolic network and on randomly generated graphs are presented.

Analyses are addressed for a number of problems in queueing systems and stochastic modeling that arose due to an investigation into techniques that could be used to approximate general closed networks. In Chapter II, a method is presented to calculate the system size distribution at an arbitrary point in time and at departures for a (n)/G/1/N queue. The analysis is carried out using an embedded Markov chain approach. An algorithm is also developed that combines our analysis with the recursive method of Gupta and Rao. This algorithm compares favorably with that of Gupta and Rao and will solve some situations when Gupta and Rao's method fails or becomes intractable. In Chapter III, an approach is developed for generating exact solutions of the time-dependent conditional joint probability distributions for a phase-type renewal process. Closed-form expressions are derived when a class of Coxian distributions are used for the inter-renewal distribution. The class of Coxian distributions was chosen so that solutions could be obtained for any mean and variance desired in the inter-renewal times. In Chapter IV, an algorithm is developed to generate numerical solutions for the steady-state system size probabilities and waiting time distribution functions of the SM/PH/1/N queue by using the matrix-analytic method. Closed form results are also obtained for particular situations of the preceding queue. In addition, it is demonstrated that the SM/PH/1/N model can be implemented to the analysis of a sequential two-queue system. This is an extension to the work by Neuts and Chakravarthy. In Chapter V, principal results developed in the preceding chapters are employed for approximate analysis of the closed network of queues with arbitrary service times. Specifically, the (n)/G/1/N queue is applied to closed networks of a general topology, and a sequential two-queue model consisting of the (n)/G/1/N and SM/PH/1/N queues is proposed for tandem queueing networks.

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Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Made available in DSpace on 2015-03-03T15:32:44Z (GMT). No. of bitstreams: 1 AnnaRSM_DISSERT.pdf: 991497 bytes, checksum: 8cd89e56b698033013c824b49f639a4e (MD5) Previous issue date: 2014-01-30 / We present a dependent risk model to describe the surplus of an insurance portfolio, based on the article "A ruin model with dependence between claim sizes and claim intervals"(Albrecher and Boxma [1]). An exact expression for the Laplace transform of the survival function of the surplus is derived. The results obtained are illustrated by several numerical examples and the case when we ignore the dependence structure present in the model is investigated. For the phase type claim sizes, we study by the survival probability, considering this is a class of distributions computationally tractable and more general / Neste trabalho apresentamos um modelo de risco dependente para descrever o excedente de uma carteira de seguros, com base no artigo "A ruin model with dependence between claim sizes and claim intervals"(Albrecher e Boxma [1]). Obtemos uma express?o exata para a probabilidade de sobreviv?ncia atrav es da Transformada de Laplace da fun??o de sobreviv?ncia do superavit. Ilustramos os resultados obtidos atrav?s de exemplos num?ricos e investigamos o que acontece ao se ignorar a estrutura de depend?ncia presente no modelo. Estudamos tamb?m a probabilidade de sobreviv?ncia para indeniza??es que possuem distribui??o do Tipo Fase, considerando que esta ? uma classe de distribui??es, computacionalmente trataveis, bem mais geral

A Markov process is defined by its transition matrix. A skip-free Markov process is a stochastic system defined by a level that can only change by one unit either upwards or downwards. A regular perturbation is defined as a modification of one or more parameters that is small enough not to change qualitatively the model.<p>This thesis focuses on a category of methods, called matrix analytic methods, that has gained much interest because of good computational properties for the analysis of a large family of stochastic processes. Those methods are used in this work in order i) to analyze the effect of regular perturbations of the transition matrix on the stationary distribution of skip-free Markov processes; ii) to determine transient distributions of skip-free Markov processes by performing regular perturbations.<p>In the class of skip-free Markov processes, we focus in particular on quasi-birth-and-death (QBD) processes and Markov modulated fluid models.<p><p>We first determine the first order derivative of the stationary distribution - a key vector in Markov models - of a QBD for which we slightly perturb the transition matrix. This leads us to the study of Poisson equations that we analyze for finite and infinite QBDs. The infinite case has to be treated with more caution therefore, we first analyze it using probabilistic arguments based on a decomposition through first passage times to lower levels. Then, we use general algebraic arguments and use the repetitive block structure of the transition matrix to obtain all the solutions of the equation. The solutions of the Poisson equation need a generalized inverse called the deviation matrix. We develop a recursive formula for the computation of this matrix for the finite case and we derive an explicit expression for the elements of this matrix for the infinite case.<p><p>Then, we analyze the first order derivative of the stationary distribution of a Markov modulated fluid model. This leads to the analysis of the matrix of first return times to the initial level, a charactersitic matrix of Markov modulated fluid models.<p><p>Finally, we study the cumulative distribution function of the level in finite time and joint distribution functions (such as the level at a given finite time and the maximum level reached over a finite time interval). We show that our technique gives good approximations and allow to compute efficiently those distribution functions.<p><p><p>----------<p><p><p><p><p><p>Un processus markovien est défini par sa matrice de transition. Un processus markovien sans sauts est un processus stochastique de Markov défini par un niveau qui ne peut changer que d'une unité à la fois, soit vers le haut, soit vers le bas. Une perturbation régulière est une modification suffisamment petite d'un ou plusieurs paramètres qui ne modifie pas qualitativement le modèle.<p><p>Dans ce travail, nous utilisons des méthodes matricielles pour i) analyser l'effet de perturbations régulières de la matrice de transition sur le processus markoviens sans sauts; ii) déterminer des lois de probabilités en temps fini de processus markoviens sans sauts en réalisant des perturbations régulières. <p>Dans la famille des processus markoviens sans sauts, nous nous concentrons en particulier sur les processus quasi-birth-and-death (QBD) et sur les files fluides markoviennes. <p><p><p><p>Nous nous intéressons d'abord à la dérivée de premier ordre de la distribution stationnaire – vecteur clé des modèles markoviens – d'un QBD dont on modifie légèrement la matrice de transition. Celle-ci nous amène à devoir résoudre les équations de Poisson, que nous étudions pour les processus QBD finis et infinis. Le cas infini étant plus délicat, nous l'analysons en premier lieu par des arguments probabilistes en nous basant sur une décomposition par des temps de premier passage. En second lieu, nous faisons appel à un théorème général d'algèbre linéaire et utilisons la structure répétitive de la matrice de transition pour obtenir toutes les solutions à l’équation. Les solutions de l'équation de Poisson font appel à un inverse généralisé, appelé la matrice de déviation. Nous développons ensuite une formule récursive pour le calcul de cette matrice dans le cas fini et nous dérivons une expression explicite des éléments de cette dernière dans le cas infini.<p>Ensuite, nous analysons la dérivée de premier ordre de la distribution stationnaire d'une file fluide markovienne perturbée. Celle-ci nous amène à développer l'analyse de la matrice des temps de premier retour au niveau initial – matrice caractéristique des files fluides markoviennes. <p>Enfin, dans les files fluides markoviennes, nous étudions la fonction de répartition en temps fini du niveau et des fonctions de répartitions jointes (telles que le niveau à un instant donné et le niveau maximum atteint pendant un intervalle de temps donné). Nous montrerons que cette technique permet de trouver des bonnes approximations et de calculer efficacement ces fonctions de répartitions. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

We derive formulas for the moments of the ruin time in a Lévy risk model and use these to determine the asymptotic behavior of the moments of the ruin time as the initial capital tends to infinity. In the special case of the perturbed Cramér-Lundberg model with phase-type or even exponentially distributed claims, we explicitly compute the first two moments of the ruin time. All our considerations distinguish between the profitable and the unprofitable setting.