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Computational Statistics Handbook With Matlab Second Edition

Author: Wendy L. Martinez
Publisher: CRC Press
ISBN: 1420010867
Size: 68.30 MB
Format: PDF, Mobi
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As with the bestselling first edition, Computational Statistics Handbook with MATLAB®, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of the algorithms in data analysis. Updated for MATLAB® R2007a and the Statistics Toolbox, Version 6.0, this edition incorporates many additional computational statistics topics. New to the Second Edition • New functions for multivariate normal and multivariate t distributions • Updated information on the new MATLAB functionality for univariate and bivariate histograms, glyphs, and parallel coordinate plots • New content on independent component analysis, nonlinear dimensionality reduction, and multidimensional scaling • New topics on linear classifiers, quadratic classifiers, and voting methods, such as bagging, boosting, and random forests • More methods for unsupervised learning, including model-based clustering and techniques for assessing the results of clustering • A new chapter on parametric models that covers spline regression models, logistic regression, and generalized linear models • Expanded information on smoothers, such as bin smoothing, running mean and line smoothers, and smoothing splines With numerous problems and suggestions for further reading, this accessible text facilitates an understanding of computational statistics concepts and how they are employed in data analysis.

Exploratory Data Analysis With Matlab

Author: Wendy L. Martinez
Publisher: CRC Press
ISBN: 9780203483374
Size: 79.46 MB
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Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited. As computational sophistication has increased, EDA has become an even more powerful process for visualizing and summarizing data before making model assumptions to generate hypotheses, encompassing larger and more complex data sets. There are many resources for those interested in the theory of EDA, but this is the first book to use MATLAB to illustrate the computational aspects of this discipline. Exploratory Data Analysis with MATLAB presents the methods of EDA from a computational perspective. The authors extensively use MATLAB code and algorithm descriptions to provide state-of-the-art techniques for finding patterns and structure in data. Addressing theory, they also incorporate many annotated references to direct readers to the more theoretical aspects of the methods. The book presents an approach using the basic functions from MATLAB and the MATLAB Statistics Toolbox, in order to be more accessible and enduring. It also contains pseudo-code to enable users of other software packages to implement the algorithms. This text places the tools needed to implement EDA theory at the fingertips of researchers, applied mathematicians, computer scientists, engineers, and statisticians by using a practical/computational approach.

Introduction To Machine Learning And Bioinformatics

Author: Sushmita Mitra
Publisher: Chapman and Hall/CRC
ISBN:
Size: 16.14 MB
Format: PDF, Kindle
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Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

Statistics And Data Analysis For Microarrays Using Matlab 2nd Edition

Author: Sorin Draghici
Publisher: Chapman and Hall/CRC
ISBN: 9781439809778
Size: 43.91 MB
Format: PDF
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Bridging the gap between introductory theory and practical knowledge, this second edition reflects the fast-moving field of DNA microarrays by adding new and updated chapters that cover cutting-edge microarray topics. This edition now offers the option of learning elements of MATLAB® in parallel with data analysis. The author also includes Bioconductor tools that are linked to the theoretical concepts discussed in the text. This edition also features more opportunities for readers to practice everything that they have learned from the book. The accompanying CD-ROM provides MATLAB code and tips on how to use the MATLAB Bioinformatics toolbox.

Statistik Workshop F R Programmierer

Author: Allen B. Downey
Publisher: O'Reilly Germany
ISBN: 3868993436
Size: 34.40 MB
Format: PDF, ePub, Docs
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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Applied Stochastic Modelling Second Edition

Author: Byron J.T. Morgan
Publisher: CRC Press
ISBN: 1420011650
Size: 80.56 MB
Format: PDF, ePub
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Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures, this edition offers numerous updates throughout. New to the Second Edition An extended discussion on Bayesian methods A large number of new exercises A new appendix on computational methods The book covers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. Although the book can be used without reference to computational programs, the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB® and R programs found in the text as well as lecture slides and other ancillary material are available for download at www.crcpress.com Continuing in the bestselling tradition of its predecessor, this textbook remains an excellent resource for teaching students how to fit stochastic models to data.

Statistische Versuchsplanung

Author: Karl Siebertz
Publisher: Springer-Verlag
ISBN: 3642054935
Size: 78.73 MB
Format: PDF, ePub, Docs
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Die statistische Versuchsplanung (Design of Experiment, DoE) ist ein Verfahren zur Analyse von (technischen) Systemen. Dieses Verfahren ist universell einsetzbar und eignet sich sowohl zur Produkt- als auch zur Prozessoptimierung, insbesondere dann, wenn viele Einflussgrößen zu berücksichtigen sind. Hauptanliegen der Autoren ist es, die Planung und Durchführung von systematischen Versuchsreihen mit engem Praxisbezug darzustellen. Industriespezifische Probleme illustrieren sie anhand zahlreicher Fallbeispiele.

A First Course In Machine Learning

Author: Simon Rogers
Publisher: CRC Press
ISBN: 1439824142
Size: 74.84 MB
Format: PDF, Kindle
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A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.

Programmieren Mit R

Author: Uwe Ligges
Publisher: Springer-Verlag
ISBN: 3540267328
Size: 29.44 MB
Format: PDF, ePub, Docs
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R ist eine objekt-orientierte und interpretierte Sprache und Programmierumgebung für Datenanalyse und Grafik - frei erhältlich unter der GPL. Ziel dieses Buches ist es, nicht nur ausführlich in die Grundlagen der Sprache R einzuführen, sondern auch ein Verständnis der Struktur der Sprache zu vermitteln. Leicht können so eigene Methoden umgesetzt, Objektklassen definiert und ganze Pakete aus Funktionen und zugehöriger Dokumentation zusammengestellt werden. Die enormen Grafikfähigkeiten von R werden detailliert beschrieben. Das Buch richtet sich an alle, die R als flexibles Werkzeug zur Datenenalyse und -visualisierung einsetzen möchten: Studierende, die Daten in Projekten oder für ihre Diplomarbeit analysieren möchten, Forschende, die neue Methoden ausprobieren möchten, und diejenigen, die in der Wirtschaft täglich Daten aufbereiten, analysieren und anderen in komprimierter Form präsentieren.