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Author : Bradley Efron,Trevor Hastie
Publisher : Cambridge University Press
Page : 513 pages
File Size : 44,7 Mb
Release : 2021-06-17
Category : Computers
ISBN : 9781108823418
Now in paperback and fortified with exercises, this brilliant, enjoyable text demystifies data science, statistics and machine learning.
Author : Bradley Efron,Trevor Hastie
Publisher : Cambridge University Press
Page : 496 pages
File Size : 50,7 Mb
Release : 2016-07-21
Category : Business & Economics
ISBN : 9781107149892
Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.
Author : Larry Wasserman
Publisher : Springer Science & Business Media
Page : 446 pages
File Size : 44,6 Mb
Release : 2013-12-11
Category : Mathematics
ISBN : 9780387217369
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 : Chester Ismay,Albert Y. Kim
Publisher : CRC Press
Page : 430 pages
File Size : 49,7 Mb
Release : 2019-12-23
Category : Mathematics
ISBN : 9781000763461
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
Author : George Casella,Roger W. Berger
Publisher : Unknown
Page : 0 pages
File Size : 47,6 Mb
Release : 2024
Category : Mathematics
ISBN : 1032593032
Basics of probability to theory of statistical inference using techniques, definitions, concepts that are statistical, natural extensions, consequences, of previous concepts. Topics from a standard inference course: distributions, random variables, data reduction, point estimation, hypothesis testing, interval estimation, regression.
Author : Bradley Efron
Publisher : Cambridge University Press
Page : 128 pages
File Size : 44,8 Mb
Release : 2012-11-29
Category : Mathematics
ISBN : 9781139492133
We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.
Author : Bradley Efron,R.J. Tibshirani
Publisher : CRC Press
Page : 456 pages
File Size : 52,9 Mb
Release : 1994-05-15
Category : Mathematics
ISBN : 0412042312
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Author : Deborah G. Mayo
Publisher : Cambridge University Press
Page : 503 pages
File Size : 40,8 Mb
Release : 2018-09-20
Category : Mathematics
ISBN : 9781107054134
Unlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors.
Author : C.S. Wallace
Publisher : Springer Science & Business Media
Page : 456 pages
File Size : 51,8 Mb
Release : 2005-05-26
Category : Computers
ISBN : 038723795X
The Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation of Occam's Razor, asserts that the ‘best’ explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data. This book gives a sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practical application. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophy of Science. Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining. C.S. Wallace was appointed Foundation Chair of Computer Science at Monash University in 1968, at the age of 35, where he worked until his death in 2004. He received an ACM Fellowship in 1995, and was appointed Professor Emeritus in 1996. Professor Wallace made numerous significant contributions to diverse areas of Computer Science, such as Computer Architecture, Simulation and Machine Learning. His final research focused primarily on the Minimum Message Length Principle.
Author : Stuart Coles
Publisher : Springer Science & Business Media
Page : 219 pages
File Size : 43,5 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781447136750
Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.
Author : David J. C. MacKay
Publisher : Cambridge University Press
Page : 694 pages
File Size : 41,6 Mb
Release : 2003-09-25
Category : Computers
ISBN : 0521642981
Table of contents
Author : Christof Koch
Publisher : MIT Press
Page : 277 pages
File Size : 48,7 Mb
Release : 2019-09-24
Category : Science
ISBN : 9780262042819
A thought-provoking argument that consciousness—more widespread than previously assumed—is the feeling of being alive, not a type of computation or a clever hack In The Feeling of Life Itself, Christof Koch offers a straightforward definition of consciousness as any subjective experience, from the most mundane to the most exalted—the feeling of being alive. Psychologists study which cognitive operations underpin a given conscious perception. Neuroscientists track the neural correlates of consciousness in the brain, the organ of the mind. But why the brain and not, say, the liver? How can the brain—three pounds of highly excitable matter, a piece of furniture in the universe, subject to the same laws of physics as any other piece—give rise to subjective experience? Koch argues that what is needed to answer these questions is a quantitative theory that starts with experience and proceeds to the brain. In The Feeling of Life Itself, Koch outlines such a theory, based on integrated information. Koch describes how the theory explains many facts about the neurology of consciousness and how it has been used to build a clinically useful consciousness meter. The theory predicts that many, and perhaps all, animals experience the sights and sounds of life; consciousness is much more widespread than conventionally assumed. Contrary to received wisdom, however, Koch argues that programmable computers will not have consciousness. Even a perfect software model of the brain is not conscious. Its simulation is fake consciousness. Consciousness is not a special type of computation—it is not a clever hack. Consciousness is about being.
Author : D. R. Cox
Publisher : Cambridge University Press
Page : 128 pages
File Size : 46,8 Mb
Release : 2006-08-10
Category : Mathematics
ISBN : 1139459139
In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.
Author : Allen B. Downey
Publisher : "O'Reilly Media, Inc."
Page : 137 pages
File Size : 47,8 Mb
Release : 2011-07-01
Category : Computers
ISBN : 9781449313104
If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data
Author : Judea Pearl,Madelyn Glymour,Nicholas P. Jewell
Publisher : John Wiley & Sons
Page : 162 pages
File Size : 43,6 Mb
Release : 2016-01-25
Category : Mathematics
ISBN : 9781119186861
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.