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Principles of Data Mining (Undergraduate Topics in Computer Science)
Principles of Data Mining (Undergraduate Topics in Computer Science)

Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.

Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each...

A Second Course in Probability
A Second Course in Probability

The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Peköz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed...

Financial Risk Modelling and Portfolio Optimization with R (Statistics in Practice)
Financial Risk Modelling and Portfolio Optimization with R (Statistics in Practice)

Introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book.

Financial Risk Modelling and Portfolio Optimization with R:

Principles of Econometrics
Principles of Econometrics
Principles of Econometrics, 4th edition, is an introductory book for undergraduate students in economics and finance, as well as for first-year graduate students in economics, finance, accounting, agricultural economics, marketing, public policy, sociology, law, and political science. It is assumed that students have taken courses in...
Visual Data Mining: The VisMiner Approach
Visual Data Mining: The VisMiner Approach

A visual approach to data mining.

Data mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed. 

This book introduces a visual methodology for data...

Foundations of Machine Learning (Adaptive Computation and Machine Learning series)
Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise...

Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics)
Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics)

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied...

Bayesian Time Series Models
Bayesian Time Series Models

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical...

Multivariate Statistical Quality Control Using R (SpringerBriefs in Statistics)
Multivariate Statistical Quality Control Using R (SpringerBriefs in Statistics)

The intensive use of automatic data acquisition system and the use of cloud computing for process monitoring have led to an increased occurrence of industrial processes that utilize statistical process control and capability analysis. These analyses are performed almost exclusively with multivariate methodologies. The aim of this Brief is to...

Handbook of Financial Risk Management: Simulations and Case Studies
Handbook of Financial Risk Management: Simulations and Case Studies

An authoritative handbook on risk management techniques and simulations as applied to financial engineering topics, theories, and statistical methodologies

The Handbook of Financial Risk Management: Simulations and Case Studies illustrates the prac­tical implementation of simulation techniques in the banking and...

Laws of Small Numbers: Extremes and Rare Events
Laws of Small Numbers: Extremes and Rare Events

Since the publication of the first edition of this seminar book in 1994, the theory and applications of extremes and rare events have enjoyed an enormous and still increasing interest. The intention of the book is to give a mathematically oriented development of the theory of rare events underlying various applications. This characteristic of...

Data Mining in Proteomics: From Standards to Applications (Methods in Molecular Biology)
Data Mining in Proteomics: From Standards to Applications (Methods in Molecular Biology)

Through the rapid development of proteomics methods and technologies, an enormous amount of data was created, leading to a wide-spread rethinking of strategy design and data interpretation. In Data Mining in Proteomics: From Standards to Applications, experts in the field present these new insights within the proteomics community, taking the...

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