Polya Urn Models

Author: Hosam Mahmoud
Publisher: CRC Press
ISBN: 9781420059847
Format: PDF, Kindle
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Incorporating a collection of recent results, Pólya Urn Models deals with discrete probability through the modern and evolving urn theory and its numerous applications. The book first substantiates the realization of distributions with urn arguments and introduces several modern tools, including exchangeability and stochastic processes via urns. It reviews classical probability problems and presents dichromatic Pólya urns as a basic discrete structure growing in discrete time. The author then embeds the discrete Pólya urn scheme in Poisson processes to achieve an equivalent view in continuous time, provides heuristical arguments to connect the Pólya process to the discrete urn scheme, and explores extensions and generalizations. He also discusses how functional equations for moment generating functions can be obtained and solved. The final chapters cover applications of urns to computer science and bioscience. Examining how urns can help conceptualize discrete probability principles, this book provides information pertinent to the modeling of dynamically evolving systems where particles come and go according to governing rules.

Stochastic Processes

Author: Pierre Del Moral
Publisher: CRC Press
ISBN: 1498701841
Format: PDF, ePub, Docs
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Unlike traditional books presenting stochastic processes in an academic way, this book includes concrete applications that students will find interesting such as gambling, finance, physics, signal processing, statistics, fractals, and biology. Written with an important illustrated guide in the beginning, it contains many illustrations, photos and pictures, along with several website links. Computational tools such as simulation and Monte Carlo methods are included as well as complete toolboxes for both traditional and new computational techniques.

Lifelong Machine Learning

Author: Zhiyuan Chen
Publisher: Morgan & Claypool Publishers
ISBN: 168173303X
Format: PDF
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Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Smart Learning Environments

Author: Maiga Chang
Publisher: Springer
ISBN: 366244447X
Format: PDF, Docs
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This book addresses main issues concerned with the future learning, learning and academic analytics, virtual world and smart user interface, and mobile learning. This book gathers the newest research results of smart learning environments from the aspects of learning, pedagogies, and technologies in learning. It examines the advances in technology development and changes in the field of education that has been affecting and reshaping the learning environment. Then, it proposes that under the changed technological situations, smart learning systems, no matter what platforms (i.e., personal computers, smart phones, and tablets) they are running at, should be aware of the preferences and needs that their users (i.e., the learners and teachers) have, be capable of providing their users with the most appropriate services, helps to enhance the users' learning experiences, and to make the learning efficient.

A Course in Statistics with R

Author: Prabhanjan N. Tattar
Publisher: John Wiley & Sons
ISBN: 1119152739
Format: PDF, ePub
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Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets


Author: Andrew C. Harvey
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3486786741
Format: PDF, ePub, Mobi
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Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Qualitative Textanalyse mit Topic Modellen

Author: Christian Papilloud
Publisher: Springer-Verlag
ISBN: 3658219807
Format: PDF, ePub
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Das Buch bietet eine Einführung in die qualitative Analyse von Texten für Geistes- und Sozialwissenschaften mit Topic-Modellen. Topic-Modelle sind probabilistische Modelle, die auf verschiedene Texte angewendet werden, um die wichtigsten Themen und die damit korrelierten Begriffe zu extrahieren, die in diesen enthalten sind. Diese Themen bilden die wichtigsten semantischen Strukturen von Texten ab, die graphisch dargestellt werden können und in Bezug auf unterschiedliche Variablen untersucht werden können. Diese Einführung erklärt die mathematischen Grundlagen von Topic-Modellen, wie sie technisch eingesetzt werden, und wie sie sich von anderen qualitativen Verfahren in den Geistes- und Sozialwissenschaften unterscheiden. Anhand von Beispielen aus der Kunst, der politischen Bewegungen, der Soziologiegeschichte und der Medizin werden die einzelnen Schritte und Techniken illustriert, die benutzt werden, um solche Verfahren zu konzipieren und sie einzusetzen. Der InhaltTopic-Modelle für qualitative Textanalysen • Durchführung von Topic-Modell-Analysen • Interviews in zwei Sprachen. Ein Beispiel aus der Kunstsoziologie • Postkarten. Topic-Modell-Analyse von dreien Texten • Textsammlung. Ein Beispiel aus der Geschichte der Soziologie • Semantische Indikatoren in quantitativen Umfragen. Ein Beispiel aus der Nanomedizin Die AutorenProf. Dr. Christian Papilloud ist Professor für Soziologie der Martin-Luther Universität Halle-Wittenberg. PD Dr. Alexander Hinneburg ist Dozent am Fachbereich Informatik der Martin-Luther Universität Halle-Wittenberg.