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Now updated in a valuable new edition—this user-friendly book focuses on understanding the «why» of mathematical statistics Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstanding new edition continues to encouragereaders to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics. Clear explanations are presented and appliedto various examples that help to impart a deeper understanding of theorems and methods—from fundamental statistical concepts to computational details. Additional features of this Second Edition include: A new chapter on random samples Coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book's FTP site Treatment of survival and hazard function, methods of obtaining estimators, and Bayes estimating Real-world examples that illuminate presented concepts Exercises at the end of each section Providing a straightforward, contemporary approach to modern-day statistical applications, Probability and Statistical Inference, Second Edition is an ideal text for advanced undergraduate- and graduate-level courses in probability and statistical inference. It also serves as a valuable reference for practitioners in any discipline who wish to gain further insight into the latest statistical tools. Получить ссылку |
Mivar NETs and logical inference with the linear complexity
Автор: Олег Варламов
Год издания:
MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and Practical Realization of Automated Constructor of Algorithms Handling More than Three Million Production Rules. The theoretical transition from the graphs of production systems to the bipartite graphs of the MIVAR nets is shown. Examples of the implementation of the MIVAR nets in the formalisms of matrixes and graphs are given. The linear computational complexity of algorithms for automated building of objects and rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets the UDAV software complex is developed, handling more than 1.17 million objects and more than 3.5 million rules on ordinary computers. The results of experiments that confirm a linear computational complexity of the MIVAR method of information processing are given.
Statistical Analysis with R For Dummies
Автор: Joseph Schmuller
Год издания:
Understanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how R offers intel from leading researchers in data science, free of charge Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data—the fast and easy way!
High-Probability Trade Setups. A Chartist's Guide to Real-Time Trading
Автор: Timothy Knight
Год издания:
A comprehensive volume of today's most profitable trade set-ups A key element of trading is identifying situations in the financial markets that can produce a profitable trade. Fundamental analysts search for situations where a stock is undervalued or if a stock's prospective earnings growth is underestimated by a majority of investors. Technical analysts look at patterns in the market that historically indicate the direction of the markets next move. High Probability Trade Set-Ups catalogs the best trading situations from a variety of technical approaches. Engaging and informative, High Probability Trade Set-Ups provides a detailed explanation of twenty powerful, recurring tradable situations in the financial markets. Each pattern is thoroughly described, including its strengths and drawbacks, ease of identification, and the degree to which it produces profitable trades. Most importantly, the author provides entry and exit points for each set up. Examples of the set-ups include chart patterns such as ascending triangles, diamonds, gaps, pennants, and channels. Features many new technical tools for tracking individual stocks Contains a resource guide, which will help to monitor the market for the trade set-ups discussed throughout the book Reveals how to use ProphetCharts to perform advanced intermarket technical studies and identify the best opportunities Written in a straightforward and accessible style, High Probability Trade Set-Ups covers a lot of ground with respect to this approach and shows you how to use it to make the most of your time in today's dynamic markets.
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A Statistical Inquiry Into the Nature and Treatment of Epilepsy
Roster and Statistical Record of Company D, of the Eleventh Regiment Maine Infantry Volunteers