Resultados de Pesquisa

INGLÊS

Clustering Methodology For Symbolic Data

de Edwin Diday e Lynne Billard

90,03€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such a data.
INGLÊS

The Rrsp Secret - Defend And Build Your Wealth With This Powerful Investment Strategy

de Greg Habstritt

29,73€ + 10% EM CARTÃO

portes grátis

 If you're a Canadian investor who's feeling ripped off lately, you're not alone. Canadians pay the highest fees in the world when it comes to investments in RRSPs and other registered accounts.
INGLÊS

Multivariate Nonparametric Regression And Visualization - With R And Applications To Finance

de Jussi Sakari Klemel

139,06€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Covering classification and regression, Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their most accurate exploration of data.
INGLÊS

Introduction To Statistical Computing - A Simulation-Based Approach

de Jochen Voss

84,73€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models.
INGLÊS

An Introduction To Statistical Computing - A Simulation-Based Approach

de Jochen (School Of Mathematics University Of Leeds, Uk) Voss

86,46€ + 10% EM CARTÃO

portes grátis

 A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models.
INGLÊS

Computational Statistics

de Geof H. Givens e Jennifer A. Hoeting

147,01€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Retaining the general organization and style of its predecessor, this new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing and computational statistics.
INGLÊS

Computational Statistics

de Geof H. (Colorado State University, Fort Collins) Givens e Jennifer A. (Colorado State University, Fort Collins) Hoeting

149,99€ + 10% EM CARTÃO

portes grátis

 Retaining the general organization and style of its predecessor, this new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing and computational statistics.
INGLÊS

Statistical And Machine Learning Approaches For Network Analysis

de Matthias Dehmer e Subhash C. Basak

144,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 * Provides a general framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for graph classification. * The proposed methods are applied to different real data sets to demonstrate their ability.
INGLÊS

Data Mining And Statistics For Decision Making

de St Phane Tuff Ry

101,96€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge.
INGLÊS

Symbolic Data Analysis - Conceptual Statistics And Data Mining

de Edwin Diday e Lynne Billard

121,83€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The first book to present a unified account of symbolic data analysis methods in a consistent statistical framework, Symbolic Data Analysis features a substantial number of examples from a range of application areas, including health, the social sciences, economics, and computer science.
INGLÊS

Large-Scale Inverse Problems And Quantification Of Uncertainty

151,35€ + 10% EM CARTÃO

portes grátis

 This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems.
INGLÊS

Multivariate Nonparametric Regression And Visualization - With R And Applications To Finance

de Jussi Sakari (Department Of Mathematical Sciences, University Of Oulu, Finland) Klemela

137,83€ + 10% EM CARTÃO

portes grátis

 Covering classification and regression, Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their most accurate exploration of data.
INGLÊS

Understanding Computational Bayesian Statistics

de William M. Bolstad

169,53€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach.
INGLÊS

Clustering Methodology For Symbolic Data

de Edwin (Universite De Paris Ix - Dauphine, France) Diday e Lynne (University Of Georgia, Athens) Billard

89,16€ + 10% EM CARTÃO

portes grátis

 Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such a data.
INGLÊS

Large-Scale Inverse Problems And Quantification Of Uncertainty

152,31€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems.
INGLÊS

Graphical Models - Representations For Learning, Reasoning And Data Mining

de Christian Borgelt, Matthias Steinbrecher e Rudolf R. Kruse

133,76€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data All basic concepts carefully explained and illustrated by examples throughout Contains background material including graphical representation, including Markov and Bayesian Networks. Includes a comprehensive bibliography.
INGLÊS

Advanced Markov Chain Monte Carlo Methods - Learning From Past Samples

de Chuanhai Liu, Faming Liang e Raymond Carroll

129,78€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 * Presents the latest developments in Monte Carlo research. * Provides a toolkit for simulating complex systems using MCMC. * Introduces a wide range of algorithms including Gibbs sampler, Metropolis-Hastings and an overview of sequential Monte Carlo algorithms.
INGLÊS

Bayesian Modeling Using Winbugs

de Ioannis Ntzoufras

182,78€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Detailed examples will be provided ranging from the very basic to the more advanced; they will also reflect realistic data sets (available from the Internet). An underlying emphasis is given to Generalized Linear Models (GLMs) that are familiar to most readers and researchers.
INGLÊS

Statistical Approach To Neural Networks For Pattern Recognition

de Robert A. Dunne

154,96€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS codes that are available on the book's related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.
INGLÊS

Data Mining Using Sas Enterprise Miner

de Randall Matignon

154,96€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner. The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis. Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4.3, followed by detailed explanations of configuration settings that are located within each node. Features of the book include: The exploration of node relationships and patterns using data from an assortment of computations, charts, and graphs commonly used in SAS procedures A step-by-step approach to each node discussion, along with an assortment of illustrations that acquaint the reader with the SAS Enterprise Miner working environment Descriptive detail of the powerful Score node and associated SAS code, which showcases the important of managing, editing, executing, and creating custom-designed Score code for the benefit of fair and comprehensive business decision-making Complete coverage of the wide variety of statistical techniques that can be performed using the SEMMA nodes An accompanying Web site that provides downloadable Score code, training code, and data sets for further implementation, manipulation, and interpretation as well as SAS/IML software programming code This book is a well-crafted study guide on the various methods employed to randomly sample, partition, graph, transform, filter, impute, replace, cluster, and process data as well as interactively group and iteratively process data while performing a wide variety of modeling techniques within the process flow of the SAS Enterprise Miner software. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike.
INGLÊS

A Statistical Approach To Neural Networks For Pattern Recognition

de Robert A. (Csiro, Australia) Dunne

158,10€ + 10% EM CARTÃO

portes grátis

 This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.
INGLÊS

Graphical Models - Representations For Learning, Reasoning And Data Mining

de Christian (European Centre For Soft Computing) Borgelt, Matthias (University Of Magdeburg, Germany) Steinbrecher e Rudolf R. (University Of Magdeburg, Germany) Kruse

136,47€ + 10% EM CARTÃO

portes grátis

 Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data All basic concepts carefully explained and illustrated by examples throughout Contains background material including graphical representation, including Markov and Bayesian Networks. Includes a comprehensive bibliography.
INGLÊS

A Practical Guide To Data Mining For Business And Industry - Case Studies And Methodology

de Andrea (Antz21 Gmbh, Gengenbach) Ahlemeyer-Stubbe e Shirley (University Of Newcastle, Uk) Coleman

ESGOTADO OU NÃO DISPONÍVEL

 * Presents data mining processes, methods and commonly used methods for descriptive and exploratory statistics using SAS and JMP.
INGLÊS

Statistical And Machine Learning Approaches For Network Analysis

de Matthias (Center For Integrative Bioinformatics, Vienna, Austria) Dehmer e Subhash C. (Natural Resources Research Institute, Usa) Basak

ESGOTADO OU NÃO DISPONÍVEL

 * Provides a general framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for graph classification. * The proposed methods are applied to different real data sets to demonstrate their ability.
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