4 edition of **Probability and statistical models** found in the catalog.

Probability and statistical models

Gupta, A. K.

- 396 Want to read
- 39 Currently reading

Published
**2010**
by Birkhäuser, Springer in [Boston], New York
.

Written in English

- Stochastic processes,
- Distribution (Probability theory),
- Mathematical statistics

**Edition Notes**

Includes bibliographical references (p. 241-243) and index.

Statement | Arjun K. Gupta, Wei-Bin Zeng, Yanhong Wu |

Contributions | Zeng, Wei-Bin, Wu, Yanhong |

Classifications | |
---|---|

LC Classifications | QA273.6 .G876 2010 |

The Physical Object | |

Pagination | x, 267 p. ; |

Number of Pages | 267 |

ID Numbers | |

Open Library | OL25337917M |

ISBN 10 | 0817649867 |

ISBN 10 | 9780817649869, 9780817649876 |

LC Control Number | 2010934423 |

OCLC/WorldCa | 646113630 |

II Statistical Inference; III Statistical Models and Methods; The first part of the book focuses on probability theory and formal language for describing uncertainty. The second part is focused on statistical inference. The third part focuses on specific methods and problems raised in the second part. The book does have a reference or. Book Description. Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions.

This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning and helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with . The new organization presents information in a logical, easy-to-grasp sequence, incorporating the latest trends and scholarship in the field of probability and statistical ed coverage of probability and statistics includes:; Five chapters that focus on probability and probability distributions, including discrete data, order statistics, multivariate distributions, and normal.

Graphical Analysis of Life Data.- Probability Plotting for Parametric Models with Uncensored Data.- Probability Plotting with Censored Data.- Non-Parametric Plotting.- Product Limit Estimator of Reliability.- Total Time on Test Plots.- Graphical Aids.- Exercises Chance regularity and statistical models 13 Statistical adequacy 16 Statistical versus theory information* 19 Observed data 20 Looking ahead 29 Exercises 30 2 Probability theory: a modeling framework 31 Introduction 31 Simple statistical model: a preliminary view 33 Probability theory: an introduction 39Cited by:

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The last reviewer must be kidding or he do not understand so much about probability. Ok, this book is great, no questions.

But this is definitely a introductory probability and statistical inference book. Going further to the probability portion, we could say that it is intended to "Calculus of Probability", not Probability by: Many of the chapters that examine central topics in applied probability can be read independently, allowing both instructors and readers extra flexibility in their use of the book.

Probability and Statistical Models is for a wide audience including advanced undergraduate and beginning-level graduate students, researchers, and practitioners in Cited by: 8. e-books in Probability & Statistics category Probability and Statistics: A Course for Physicists and Engineers by Arak M.

Mathai, Hans J. Haubold - De Gruyter Open, This is an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing.

Probability models are now a vital componentof every scienti c investigation. This book is intended to introduce basic ideas in stochastic modeling, with emphasis on models and techniques. These models lead to well-known parametric lifetime distributions. Probability and Statistical Models with Applications - Probability and statistical models book Press Book This monograph of carefully collected articles reviews recent developments in theoretical and applied statistical science, highlights current noteworthy results and illustrates their applications; and points out possible new directions to pursue.

Many of the chapters that examine central topics in applied probability can be read independently, allowing both instructors and readers extra flexibility in their use of the book. Probability and Statistical Models is for a wide audience including advanced undergraduate and beginning-level graduate students, researchers, and practitioners in.

图书Probability and Statistics 介绍、书评、论坛及推荐. The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data/10(95).

A statistical model is a probability distribution constructed to enable infer-ences to be drawn or decisions made from data. This idea is the basis of most tools in the statistical workshop, in which it plays a central role by providing economical and File Size: KB.

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population).A statistical model represents, often in considerably idealized form, the data-generating process.

A statistical model is usually specified as a mathematical relationship between one or more random.

Probability Density Function Cumulative Distribution Function Pareto Distribution Normal Probability Plot Tail Index These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm : David Ruppert.

The difference between statistical and probabilistic models. A probabilistic analysis is possible when we know a good generative model for the randomness in the data, and we are provided with the parameters’ actual values.

Figure The probabilistic model we obtained in Chapter data are represented as \(x\) in green. We can use the observed data to compute the.

About this book A comprehensive text and reference bringing together advances in the theory of probability and statistics and relating them to applications. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression models, multivariate regression models, and.

This is the first of two books on the statistical theory of reliability and life testing. The present book concentrates on probabilistic aspects of reliability theory, while the forthcoming book will focus on inferential aspects of reliability and life testing, applying the probabilistic tools developed in this volume.

This book emphasizes the newer, research aspects of reliability theory. Models and likelihood are the backbone of modern statistics. This book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners.

Preface. This is an Internet-based probability and statistics materials, tools and demonstrations presented in this E-Book would be very useful for advanced-placement (AP) statistics educational E-Book is initially developed by the UCLA Statistics Online Computational Resource (SOCR).However, all statistics instructors, researchers and.

As aresult, the book concentrates on the methodology of the subject and on understanding theoretical results rather than on its theoretical development. An intrinsic aspect of reliability analysis is that the failure of components is best modelled using techniques drawn from probability and : Springer-Verlag New York.

Statistical models have a number of parameters that can be modified. For example, a coin can be represented as samples from a Bernoulli distribution, which models two possible outcomes.

The Bernoulli distribution has a single parameter equal to the probability of one outcome, which in most cases is the probability of landing on heads. Preface This book is an introductory text on probability and statistics, targeting students who. Book: Statistical modeling: a fresh approach by Daniel Kaplan.

The Probability and statistics cookbook, by Matthias Vallentin. The NIST Engineering Statistics Handbook, an online compendium of information on statistics useful for engineering analysis. Seeing theory, a visual introduction to basic concepts in probability and statistics.

An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution.

The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data.Learn statistics and probability for free—everything you'd want to know about descriptive and inferential statistics.

Full curriculum of exercises and videos. If you're seeing this message, it means we're having trouble loading external resources on our website.The book presents subjects such as "maximum likelihood and sufficiency," and is written with an intuitive, heuristic approach to build reader comprehension.

It also includes many probability inequalities that are not only useful in the context of this text, but also as a resource for investigating convergence of statistical procedures.