Statistics

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and...

Introductory Relational Database Design for Business with Microsoft Access takes a different approach to relational database design in that it develops understanding step-by-step, through numerous compact but realistic examples which build gradually in...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781118337769Author S CoakesFormat PaperbackPage Count 288Imprint John Wiley & Sons Australia LtdPublisher John Wiley & Sons Australia LtdWeight(grams)...

Praise for the Third Edition "This book provides in-depth coverage of modelling techniques used throughout many branches of actuarial science...The exceptional high standard of this book has made it a pleasure to read." --Annals of Actuarial...

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based...

This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to...

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest...

A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and...

There is no book currently available that gives a comprehensive treatment of the design, construction, and use of index numbers. However, there is a pressing need for one in view of the increasing and more sophisticated employment of index numbers in the...

Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications introduces the outlook and extension toward deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors' 20 years of...

This first course in statistics is designed for undergraduate students. There are dozens of statistics textbooks in the market. But most of these textbooks are either pitched at a level that is too high or too low for most undergraduate students. Many...

This book is a collection of applications of analytic techniques to a number of popular sports including baseball, basketball, hockey, Jai Alai, NFL football and horseracing. We focus on both the statistics of the sporting events and betting strategies...

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as...

SAS for Elementary Statistics: Getting Started provides an introduction to SAS programming for those who have experience with introductory statistical methods. It is also an excellent programming supplement for an introductory statistics course. It is...

Unstructured Mining Approaches to Solve Complex Scientific ProblemsAs the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new...

Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of...

Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and...

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference...

Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion,...

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of...

Cyberspace is changing the face of crime. For criminals it has become a place for rich collaboration and learning, not just within one country; and a place where new kinds of crimes can be carried out, and a vehicle for committing conventional crimes...

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications...

Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when...

Since ROC curves have become ubiquitous in many application areas, the various advances have been scattered across disparate articles and texts. ROC Curves for Continuous Data is the first book solely devoted to the subject, bringing together all the...

The increasing complexity of insurance and reinsurance products has seen a growing interest amongst actuaries in the modeling of dependent risks. For efficient risk management, actuaries need to be able to answer fundamental questions such as: Is the...

Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications...

This book aims to teach core concepts of machine learning while focuses on modern applications. It is aimed at those who want to master machine learning by providing technical and practical insights. It avoids using complicated mathematics to explain how...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780730312932Author Ken BlackFormat PaperbackPage Count 640Imprint John Wiley & Sons Australia LtdPublisher John Wiley & Sons Australia...

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. ...

'Machine Learning for Knowledge Discovery with R' contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes most recent supervised and unsupervised machine learning methodologies such as...

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study,...

Expertise in massive scale graph analytics is key for solving real-world grand challenges from health to sustainability to detecting insider threats, cyber defense, and more. Massive Graph Analytics provides a comprehensive introduction to massive graph...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780133022469Author David F. GroebnerFormat PaperbackPage Count 464Imprint PearsonPublisher Pearson Education (US)Weight(grams) 1102g

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest...

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its...

1) Extensive compilation of commonly used statistical methods from fundamental to advancedlevel, which are essential for applied data scientists and practitioners in data science2) Straightforward explanations of the collected statistical theory and...

R for College Mathematics and Statistics encourages the use of R in mathematics and statistics courses. Instructors are no longer limited to ``nice'' functions in calculus classes. They can require reports and homework with graphs. They can do...

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about...

In the modern world, data is a vital asset for any organization, regardless of industry or size. The world is built upon data. However, data without knowledge is useless. The aim of this book, briefly, is to introduce new approaches that can be used to...

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors—all highly experienced...