EBookClubs

Read Books & Download eBooks Full Online

EBookClubs

Read Books & Download eBooks Full Online

Book Black Belt

    Book Details:
  • Author :
  • Publisher :
  • Release : 1974-02
  • ISBN :
  • Pages : 64 pages

Download or read book Black Belt written by and published by . This book was released on 1974-02 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: The oldest and most respected martial arts title in the industry, this popular monthly magazine addresses the needs of martial artists of all levels by providing them with information about every style of self-defense in the world - including techniques and strategies. In addition, Black Belt produces and markets over 75 martial arts-oriented books and videos including many about the works of Bruce Lee, the best-known marital arts figure in the world.

Book Applied Bayesian Modelling

Download or read book Applied Bayesian Modelling written by Peter Congdon and published by John Wiley & Sons. This book was released on 2014-06-25 with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.

Book Black Belt

    Book Details:
  • Author :
  • Publisher :
  • Release : 1997-11
  • ISBN :
  • Pages : 176 pages

Download or read book Black Belt written by and published by . This book was released on 1997-11 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The oldest and most respected martial arts title in the industry, this popular monthly magazine addresses the needs of martial artists of all levels by providing them with information about every style of self-defense in the world - including techniques and strategies. In addition, Black Belt produces and markets over 75 martial arts-oriented books and videos including many about the works of Bruce Lee, the best-known marital arts figure in the world.

Book Reports

Download or read book Reports written by National Interfraternity Conference and published by . This book was released on 1924 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Michiganensian

Download or read book Michiganensian written by and published by UM Libraries. This book was released on 1932 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Baird s Manual of American College Fraternities

Download or read book Baird s Manual of American College Fraternities written by and published by . This book was released on 1927 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Core Statistics

Download or read book Core Statistics written by Simon N. Wood and published by Cambridge University Press. This book was released on 2015-04-13 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Core Statistics is a compact starter course on the theory, models, and computational tools needed to make informed use of powerful statistical methods.

Book Bayesian Statistical Modelling

Download or read book Bayesian Statistical Modelling written by Peter Congdon and published by John Wiley & Sons. This book was released on 2007-04-04 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

Book The Core Theory in Economics

Download or read book The Core Theory in Economics written by Lester G. Telser and published by Routledge. This book was released on 2007 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this key title, Lester Telser, the world's core theorist, explores several distinct areas to skilfully bring the ideas of core theory to bear on a range of issues within economics - with particular emphasis on supply and demand and the way markets function.

Book House of Kidz

    Book Details:
  • Author : Colin Cohen
  • Publisher : CC 600, Inc.
  • Release : 1999
  • ISBN : 9780966472424
  • Pages : 350 pages

Download or read book House of Kidz written by Colin Cohen and published by CC 600, Inc.. This book was released on 1999 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Bayesian Psychometric Modeling

Download or read book Bayesian Psychometric Modeling written by Roy Levy and published by CRC Press. This book was released on 2017-07-28 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.

Book NASA Reference Publication

Download or read book NASA Reference Publication written by and published by . This book was released on 1977 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Black Belt

    Book Details:
  • Author :
  • Publisher :
  • Release : 1997-11
  • ISBN :
  • Pages : 176 pages

Download or read book Black Belt written by and published by . This book was released on 1997-11 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The oldest and most respected martial arts title in the industry, this popular monthly magazine addresses the needs of martial artists of all levels by providing them with information about every style of self-defense in the world - including techniques and strategies. In addition, Black Belt produces and markets over 75 martial arts-oriented books and videos including many about the works of Bruce Lee, the best-known marital arts figure in the world.

Book Bayesian Inference for Stochastic Processes

Download or read book Bayesian Inference for Stochastic Processes written by Lyle D. Broemeling and published by CRC Press. This book was released on 2017-12-12 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.

Book Introduction to WinBUGS for Ecologists

Download or read book Introduction to WinBUGS for Ecologists written by Marc Kéry and published by Academic Press. This book was released on 2010-07-19 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS Provides every detail of R and WinBUGS code required to conduct all analyses Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)

Book Advanced Numerical Techniques for Photonic Crystals

Download or read book Advanced Numerical Techniques for Photonic Crystals written by Didier Felbacq and published by Morgan & Claypool Publishers. This book was released on 2016-12-07 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a set of theoretical and numerical tools useful for the study of wave propagation in metamaterials and photonic crystals. While concentrating on electromagnetic waves, most of the material can be used for acoustic (or quantum) waves. For each presented numerical method, numerical code written in MATLAB® is presented. The codes are limited to 2D problems and can be easily translated in Python or Scilab, and used directly with Octave as well.

Book Bayesian Inference for Probabilistic Risk Assessment

Download or read book Bayesian Inference for Probabilistic Risk Assessment written by Dana Kelly and published by Springer Science & Business Media. This book was released on 2011-08-30 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.