Understanding Probability and Statistics
Author : Ruma Falk
Publisher : A K Peters/CRC Press
Page : 264 pages
File Size : 38,91 MB
Release : 1993-04-15
Category : Mathematics
ISBN :
Author : Ruma Falk
Publisher : A K Peters/CRC Press
Page : 264 pages
File Size : 38,91 MB
Release : 1993-04-15
Category : Mathematics
ISBN :
Author : Yu. M. Suhov
Publisher : Cambridge University Press
Page : 477 pages
File Size : 48,19 MB
Release : 2014-09-22
Category : Mathematics
ISBN : 1107603587
A valuable resource for students and teachers alike, this second edition contains more than 200 worked examples and exam questions.
Author : Larry Wasserman
Publisher : Springer Science & Business Media
Page : 446 pages
File Size : 11,43 MB
Release : 2013-12-11
Category : Mathematics
ISBN : 0387217363
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Author : John Tabak
Publisher : Infobase Publishing
Page : 241 pages
File Size : 23,32 MB
Release : 2014-05-14
Category : Electronic books
ISBN : 0816068739
Presents a survey of the history and evolution of the branch of mathematics that focuses on probability and statistics, including useful applications and notable mathematicians in this area.
Author : Richard Von Mises
Publisher : Courier Corporation
Page : 273 pages
File Size : 30,17 MB
Release : 1981-01-01
Category : Mathematics
ISBN : 0486242145
This comprehensive study of probability considers the approaches of Pascal, Laplace, Poisson, and others. It also discusses Laws of Large Numbers, the theory of errors, and other relevant topics.
Author : F.M. Dekking
Publisher : Springer Science & Business Media
Page : 485 pages
File Size : 49,24 MB
Release : 2006-03-30
Category : Mathematics
ISBN : 1846281687
Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
Author : Michael J. Evans
Publisher : Macmillan
Page : 704 pages
File Size : 18,61 MB
Release : 2004
Category : Mathematics
ISBN : 9780716747420
Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.
Author : Nitis Mukhopadhyay
Publisher : CRC Press
Page : 694 pages
File Size : 35,50 MB
Release : 2020-08-30
Category : Mathematics
ISBN : 1000291553
Priced very competitively compared with other textbooks at this level! This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. Beginning wi
Author : Hossein Pishro-Nik
Publisher :
Page : 746 pages
File Size : 37,70 MB
Release : 2014-08-15
Category : Probabilities
ISBN : 9780990637202
The book covers basic concepts such as random experiments, probability axioms, conditional probability, and counting methods, single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristic functions, random vectors, and inequalities; limit theorems and convergence; introduction to Bayesian and classical statistics; random processes including processing of random signals, Poisson processes, discrete-time and continuous-time Markov chains, and Brownian motion; simulation using MATLAB and R.
Author : Richard McElreath
Publisher : CRC Press
Page : 488 pages
File Size : 18,43 MB
Release : 2018-01-03
Category : Mathematics
ISBN : 1315362619
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.