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| Author | : Yu. M. Suhov |
| Publisher | : Cambridge University Press |
| Total Pages | : 477 |
| Release | : 2014-09-22 |
| Genre | : Mathematics |
| ISBN | : 1107603587 |
A valuable resource for students and teachers alike, this second edition contains more than 200 worked examples and exam questions.
| Author | : Biswas D |
| Publisher | : New Central Book Agency |
| Total Pages | : 500 |
| Release | : 2009 |
| Genre | : Probabilities |
| ISBN | : 9788173814945 |
| Author | : J.G. Kalbfleisch |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 355 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461210968 |
A carefully written text, suitable as an introductory course for second or third year students. The main scope of the text guides students towards a critical understanding and handling of data sets together with the ensuing testing of hypotheses. This approach distinguishes it from many other texts using statistical decision theory as their underlying philosophy. This volume covers concepts from probability theory, backed by numerous problems with selected answers.
| Author | : J.G. Kalbfleisch |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 321 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1468400916 |
| Author | : Kunihiro Suzuki |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2019 |
| Genre | : Mathematical statistics |
| ISBN | : 9781536144628 |
We utilize statistics in our daily lives when we evaluate TV program ratings, predict voting outcomes, prepare stock, predict the amounts of sales, and evaluate the effectiveness of medical treatment. We predict the result not on the basis of personal experience, but on the basis of data. However, the accuracy of the prediction depends on the data, the theory, and the depth of understanding the model. In this book, the author analyzes fundamental models to advanced models without skipping their derivation processes. It is then possible to clearly understand the assumption and approximations used in the model, and hence understand the limitation of the model. We also cover almost all of the subjects in statistics since they are all related to each other. Although this book treats advanced models, people who are not professional in science can easily understand the content since by stepping up the derivation from the fundamental level to the advanced level. The author does hope that readers can understand the meaning of the models in statistics and techniques to reach the final results.
| Author | : Guy Lebanon |
| Publisher | : |
| Total Pages | : 346 |
| Release | : 2012-10-09 |
| Genre | : Machine learning |
| ISBN | : 9781479344765 |
Introduction to probability theory with an emphasis on the multivariate case. Includes random vectors, random processes, Markov chains, limit theorems, and related mathematics such as metric spaces, measure theory, and integration.
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| Author | : F.M. Dekking |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 485 |
| Release | : 2006-03-30 |
| Genre | : 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 | : Ronald A. Howard |
| Publisher | : Courier Corporation |
| Total Pages | : 610 |
| Release | : 2012-05-04 |
| Genre | : Mathematics |
| ISBN | : 0486140679 |
This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. Its intent is to equip readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics and space engineering to marketing. More than a collection of techniques, it constitutes a guide to the consistent application of the fundamental principles of probability and linear system theory. Author Ronald A. Howard, Professor of Management Science and Engineering at Stanford University, begins with the basic Markov model, proceeding to systems analyses of linear processes and Markov processes, transient Markov processes and Markov process statistics, and statistics and inference. Subsequent chapters explore recurrent events and random walks, Markovian population models, and time-varying Markov processes. Volume I concludes with a pair of helpful indexes.
| Author | : Larry Wasserman |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 446 |
| Release | : 2013-12-11 |
| Genre | : 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.
