Îïèñàíèå: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.
Àâòîð: Koralov Íàçâàíèå: Theory of Probability and Random Processes ISBN: 3540254846 ISBN-13(EAN): 9783540254843 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 8909 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: A one-year course in probability theory and the theory of random processes, taught at Princeton University to undergraduate and graduate students, forms the core of the content of this bookIt is structured in two parts: the first part providing a detailed discussion of Lebesgue integration, Markov chains, random walks, laws of large numbers, limit theorems, and their relation to Renormalization Group theory. The second part includes the theory of stationary random processes, martingales, generalized random processes, Brownian motion, stochastic integrals, and stochastic differential equations. One section is devoted to the theory of Gibbs random fields.This material is essential to many undergraduate and graduate courses. The book can also serve as a reference for scientists using modern probability theory in their research.
Îïèñàíèå: Updated to conform to Mathematica® 7.0, this second edition shows how to easily create simulations from templates and solve problems using Mathematica. Along with new sections on order statistics, transformations of multivariate normal random variables, and Brownian motion, this edition offers an expanded section on Markov chains, more example data of the normal distribution, and more attention on conditional expectation. It also includes additional problems from Actuarial Exam P as well as new examples, exercises, and data sets. The accompanying CD-ROM contains updated Mathematica notebooks and a revised solutions manual is available for qualifying instructors.
Îïèñàíèå: This handbook brings together a comprehensive collection of mathematical material in one location. It also offers a variety of new results interpreted in a form that is particularly useful to engineers, scientists, and applied mathematicians.
Àâòîð: Douglas C. Montgomery,Cheryl L. Jennings,Murat Kul Íàçâàíèå: Introduction to Time Series Analysis and Forecasting ISBN: 1118745116 ISBN-13(EAN): 9781118745113 Èçäàòåëüñòâî: Wiley Ðåéòèíã: Öåíà: 19421 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: Praise for the First Edition" [t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." ?ˆ“MAA ReviewsThoroughly updated throughout, Introduction to Time Series Analysis
Àâòîð: Baclawski, Kenneth P. Íàçâàíèå: Introduction to probability with r ISBN: 1420065211 ISBN-13(EAN): 9781420065213 Èçäàòåëüñòâî: Taylor&Francis Ðåéòèíã: Öåíà: 17241 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: Presents R programs and animations to provide an understanding of how to model natural phenomena from a probabilistic point of view. This work centers on viewing probability as a way to look at the world and shows how to combine and link stochastic processes to form complex processes that are better models of natural phenomena.
Àâòîð: Chapra, Steven C. Íàçâàíèå: Numerical methods for engineers ISBN: 007126759X ISBN-13(EAN): 9780071267595 Èçäàòåëüñòâî: McGraw-Hill Ðåéòèíã: Öåíà: 9799 ð. Íàëè÷èå íà ñêëàäå: Íåâîçìîæíà ïîñòàâêà.
Îïèñàíèå: A textbook presenting notions and ideas at the foundations of a statistical treatment of risks. The text is unlike that found in traditional mathematics literature and differs from typical textbooks in its verbal approach to many explanations and examples.
Îïèñàíèå: Introduction to Probability and Statistics for Engineers and Scientists, Fifth Edition is a proven text reference that provides a superior introduction to applied probability and statistics for engineering or science majors. The book lays emphasis in the manner in which probability yields insight into statistical problems, ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data from actual studies across life science, engineering, computing and business are incorporated in a wide variety of exercises and examples throughout the text. These examples and exercises are combined with updated problem sets and applications to connect probability theory to everyday statistical problems and situations. The book also contains end of chapter review material that highlights key ideas as well as the risks associated with practical application of the material. Furthermore, there are new additions to proofs in the estimation section as well as new coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions. This text is intended for upper level undergraduate and graduate students taking a course in probability and statistics for science or engineering, and for scientists, engineers, and other professionals seeking a reference of foundational content and application to these fields.
Îïèñàíèå: This user--friendly resource helps readers grasp the concepts of probability and stochastic processes, so they can apply them in professional engineering practice. The book presents concepts clearly as a sequence of building blocks that are identified either as an axiom, definition, or theorem. This approach provides a better understanding of the material, which can be used to solve practical problems.
Îïèñàíèå: A Hands-On Approach to Teaching Introductory Statistics Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets. New to the Second Edition A new chapter on non-linear regression models A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken Additional exercises in most chapters A summary of statistical formulas related to the specific designs used to teach the statistical concepts This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R. For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences.
