• Categories • • (109) • (102) • (102) • (93) • (86) • (81) • (78) • (67) • (62) • (60) • (58) • (47) • (47) • (42) • (37) • (28) • (27) • (26) • (25) • (25) • (24) • (23) • (23) • (22) • (22) • (19) • (19) • (19) • (19) • (18) • (17) • (17) • (17) • (16) • (15) • (14) • (11) • (11) • (11) • (10) • (10) • (9) • (8) • (8) • (8) • (7) • (7) • (7) • (7) • (6) • (6) • (6) • (5) • (5) • (5) • (5) • (5) • (4) • (4) • (4) • (3) • (3) • (3) • (3) • (3) • (3) • (3) • (3) • (3) • (2) • (2) • (2) • (2) • (2) • (2) • (2) • (1) • (1) • (1) • (1) • (1) • (1) • (1) • (1) • (1) • (1). Neural Network Design (2nd Edition), by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. This book gives an introduction to basic neural network architectures and learning rules. Emphasis is placed on the mathematical analysis of these networks, on methods of training them and on their application to practical engineering problems in such areas as nonlinear regression, pattern recognition, signal processing, data mining and control systems.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework.

Author by: R.A. Thisted Language: en Publisher by: Routledge Format Available: PDF, ePub, Mobi Total Read: 59 Total Download: 949 File Size: 55,7 Mb Description: Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory.

At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing.

The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques. Author by: Maria L. Rizzo Language: en Publisher by: CRC Press Format Available: PDF, ePub, Mobi Total Read: 23 Total Download: 908 File Size: 42,9 Mb Description: Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach.

Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. After an overview of computational statistics and an introduction to the R computing environment, the book reviews some basic concepts in probability and classical statistical inference. Each subsequent chapter explores a specific topic in computational statistics. These chapters cover the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions.

Download PDF (version 0. It is aimed for upper level undergraduate students, masters students and Ph. Statistics Using R with Biological Examples. ISBN 1- (alk. Gentle: Elements of Computational Statistics. Pdf ProbForecastGOP/doc/vignette.

Focusing on implementation rather than theory, this text serves as a balanced, accessible introduction to computational statistics and statistical computing. Author by: Debasis Kundu Language: en Publisher by: Alpha Science Int'l Ltd. Format Available: PDF, ePub, Mobi Total Read: 22 Total Download: 230 File Size: 54,8 Mb Description: Statistical Computing: Existing Methods and Recent Developments attempts to provide a state of the art account of existing methods and recent developments in the so called new field of Statistical Computing. Fourteen different chapters deal with a wide range of topics. This includes introductory topics such as the basic numerical analysis methods, random number generation, graphical techniques used in statistical data analysis and other areas.

Download De Filmes Gratis No Celular. It also covers the more specialized techniques such as the EM algorithm, genetic algorithms, nonparametric smoothing techniques, resampling methods, and artificial neural network models, to name a few. In addition, the volume also deals with the computational issues involved in the analysis of mixture models, adaptive designs, weighted distributions, and statistical signal processing, topics which are unlikely to be covered in a standard text on Statistical Computing.

Author by: Michael J. Crawley Language: en Publisher by: Wiley Format Available: PDF, ePub, Mobi Total Read: 38 Total Download: 309 File Size: 51,9 Mb Description: Many statistical modelling and data analysis techniques can be difficult to grasp and apply, and it is often necessary to use computer software to aid the implementation of large data sets and to obtain useful results. S-Plus is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply a number of statistical methods, ranging from simple regression to time series or multivariate analysis. This text offers extensive coverage of many basic and more advanced statistical methods, concentrating on graphical inspection, and features step-by-step instructions to help the non-statistician to understand fully the methodology.

Author by: Randall L. Eubank Language: en Publisher by: CRC Press Format Available: PDF, ePub, Mobi Total Read: 78 Total Download: 980 File Size: 48,6 Mb Description: With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a 'boot-camp' on the use of C++ and R.

The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R.

The book lays the foundation for original code development in a research environment. Author by: Luke Tierney Language: en Publisher by: John Wiley & Sons Format Available: PDF, ePub, Mobi Total Read: 47 Total Download: 415 File Size: 42,6 Mb Description: Written for the professional statistician or graduate statistics student, the primary objective of this book is to describe a system, based on the LISP language, for statistical computing and dynamic graphics to show how it can be used as an effective platform for a wide range of statistical computing tasks ranging from basic calculations to customizing dynamic graphs. In addition, it introduces object-oriented programming and graphics programming in a statistical context. The discussion of these ideas is based on the Lisp-Stat system; readers with access to such a system can reproduce the examples presented and use them as a basis for further experimentation and study.

Author by: Robert A. Stine Language: en Publisher by: Sage Publications, Inc Format Available: PDF, ePub, Mobi Total Read: 83 Total Download: 507 File Size: 40,8 Mb Description: The nature of statistics has changed from classical notions of hypothesis testing, towards graphical and exploratory data analysis which exploits the flexibility of interactive computing and graphical displays. This book describes seven statistical computing environments - APL2STAT, GAUSS, Lisp-Stat, Mathematica, S, SAS//IML, and Stata - which can be used effectively in graphical and exploratory modeling. These statistical computing environments, in contrast to standard statistical packages, provide programming tools for building other statistical applications. Programmability, flexible data structures, and - in the case of some of the computing environments - graphical interfaces and object-oriented programming, permit res. Author by: Mark P. Faronics Anti Executable Con Crack.

Van der Loo Language: en Publisher by: Packt Publishing Ltd Format Available: PDF, ePub, Mobi Total Read: 31 Total Download: 481 File Size: 48,8 Mb Description: A practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio. The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality to ease their development efforts. Familiarity with R is assumed. Those who want to get started with R development using RStudio will also find the book useful.

Even if you already use R but want to create reproducible statistical analysis projects or extend R with self-written packages, this book shows how to quickly achieve this using RStudio.