Product Description
An innovative book that teaches in a tutorial manner. Acting as tutor, examples are presented, explained and solved in detail, providing the student with ample opportunities for reinforcement of the material. The book consists of 46 lessons covering descriptive statistics, set theory, probability theory and inference theory. ... Read more

Customer Reviews (1)

Tutorial approach to learn Statistics
Statistics & Probability Theory by Howard Dachslager is indeed the best math book I¡¦ve ever studied from.When I¡¦m studying from the book, it feels like I¡¦ve hired a tutor because every problem is shown step-by-step.I just love how the book matches its example problems with practice problems because when I run into practice problems I don¡¦t understand, I can always rely on going back to the example problems.With this book in hand, I don¡¦t think anyone really needs to go to class to learn Statistics because the book is so easy to comprehend and learn from.To be honest, if you own this book, you will definitely find Statistics easy and fun. ... Read more

Product Description
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.

This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.

The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'. ... Read more

Customer Reviews (3)

good
This book is not really a tutorial for beginners as it goes directly into the subject. It is well written, rigorous, and not that expensive for people needing to learn the bayesian principles. For total beginners as I was, I would advise reading "Introduction to Bayesian Statistics" by Bolstad before this one. A good book on the topic, with good ideas and recent developments !

Very good introduction
This book is a must for those that are introducing themselves in bayesian statistics. It goes very strightforward in to the main topics and the mathematical notation is easy to follow. If you are just beginning I would recommend to read this book before Jaynes' book Probability Theory: The Logic of Science and after William M. Bolstad's Introduction to Bayesian Statistics

In-Depth and Practical
Sivia and Skilling give a concise and clear exposition of Bayesian statistical analysis, and pair it with practical, real examples.It has been a great aid to me in doing actual data work.This text gets the balance of theoretical detail and practicality just right.In particular, abandoning the usual emphasis on analytical solutions and instead pairing real examples with numerical solution algorithms when appropriate, is perfect for someone concerned with applying Bayesian statistical analysis to real problems.A great and genuinely useful book! ... Read more

Product Description
This comprehensive software tutorial program and exercise manual makes use of the computer to tackle and present to readers conceptually-difficult statistical concepts. Users are introduced to a variety of difficult concepts in a truly dynamic way. ... Read more

Product Description
This CD-ROM contains live authored questions and supporting questions and answers, which are linked to the books in the series. An example is that the tutor talks students through a solution, whilst the workings are simultaneously written on a white board. ... Read more

Product Description
Renowned for its clear prose and no-nonsenseemphasis on core concepts, Statistics coversfundamentals using real examples to illustratethe techniques.The Fourth Edition has beencarefully revised and updated to reflect current data. ... Read more

Customer Reviews (32)

Accessible and instructive
This is a great book for learning basic statistics and the scientific method without getting too bogged down in higher math. Don't be scared by the cover - much of the book is in prose - and highly readable prose at that. It provides very useful examples for understanding the basics of statistical models and experimental design. Definitely a great way to ease into an intimidating but important subject.

Excellent basic learning for statistics
The book is a classic. It is well written and explains basics of statistics very well -The language is very simple and the concepts explained are easy to grasp. I would recommend this book strongly for anyone interested in learning from scratch or as a refresher. You can use this book for self learning too.

Online Class
Using this book for an online course. Not the same edition as the teacher wanted, but good enough. Has all the same problems and information.

textbook use
brand new, fast shipping
contents are really good for students taking statistic class and economics

Best examples of any statistics textbook
When I initially looked at this textbook, I was disappointed that it has the least extensive number of statistical tests described. There is only a passing mention of Fisher's exact test, no logistic regression, etc. However the examples are by far the best I've seen in any textbook (even non statistics books) and they are very helpful in understanding statistical pitfalls. They make you think more than most advanced texts. The clarity of writing is superb. Since many statistical problems are due to the setup and reasoning of studies, rather than picking the wrong text, this textbook will go far to enable the reader to more critically understand the methodology of studies. ... Read more

Product Description
This is the first book on the maximum entropy and Bayesian methods aimed at senior undergraduates in science and engineering. It takes the mystery out of statistics by showing how a few fundamental rules can be used to tackle a wide variety of problems in data analysis.After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing.Other topics covered include reliability analysis, multivariate optimization, least squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy, and experimental design. As a logical and unified approach to the subject of data analysis, with a self-contained tutorial approach, this work will be valued by instructors and students alike. ... Read more

Customer Reviews (7)

concise but clear
Sivia offers a brief but thorough explanation of how to use Bayesians in data analysis. He illustrates with important examples that commonly often arise in the sciences. As in estimating the true amplitude of a signal in the presence of background noise. These days, for anyone in a lab sitting next to an electronic gadget acquiring data, you can surely emphathise with this problem.

The necessary background for his book includes being familiar with multivariable calculus. Specifically, with the Taylor expansion in several variables, and with the Jacobian matrix of second partial derivatives. Plus of course a grounding in statistics, including maximum likelihood estimations and the normal distribution.

A gem.
This tutorial on Bayesian data analysis is a gem: very terse, yet explaining the concepts very clearly, giving many insightful examples along the way. This is achieved within only 180 pages by focussing on understanding and intuition instead of mathematical formalism. After reading this tutorial, the reader will be familiar with the way of thinking in Bayesian statistics. The tutorial thus encourages the reader to get more independent from the (conceptually more complicated) cook book statistics with the associated risk of misusage. When reading this book I felt as if a whole jumble of more or less unconnected pieces of statistical wisdom was finally falling into place within the Bayesian framework.

A few critical remarks: (1) A clearer structure with more informative section and subsection headings would help to quicker find things and keep the material orderly in one`s mind. (As an example, the two core chapters are entitled „Parameter estimation I" and„Parameter estimation II").(2) The chapter on non-paramteric estimation is much harder to understand than the first six chapters. This is in part justified by the advancedness of the topic but it could profit from a streamlining (and updating).(3) This book certainly would have the chance to become much more popular than it is now if it was more reasonably priced.

The reader should have a firm command of elementary probability theory, first year calculus (Taylor expansion, multidimensional integration, finding the maximum of a multi-variable function), as well as elementary linear algebra (diagonalization, eigenvectors, determinants). Ideally, she should be familiar with basic classical statistics, as this will make her appreciate the elegance of the Bayesian view more. Physicists will love this book.

Bayes' Theorem made simple
This is an excellent tutorial for the both the beginner (undergraduate) and more advanced scientist.Sivia takes the reader through several examples with simple and conciseexplanations.I have used many of the examples discussed in the book as starting points for problems that I have encountered in my work.I would recommend giving it a try...

Learn what it means to be a "Bayesian"
For years I listened to people present "Bayesian" solutions to problems without appreciating the subtler implications of the term.Bayes' theorem is one of the first topics taught in freshman-level probability and statistics.It's taught, and it's used, but it isn't a central part of the teaching of modern statistics.

Bayesians make it central.Sivia does a masterful job of deriving most of statistics from judicious applications of Bayes' theorem.He can do this, in part, because the visible universe is finite.Infinities and limit theorems can be bypassed, and previously impossible functional forms become workable.

The book is a tutorial; you have to think.But it's well worth it.

poor pedagogy
Maybe it's just me but I found this book not very helpful. The easy stuff is repeated often (Bayes's theorem is quoted every few pages) but when a difficulty arises it is glossed over. Maybe it gets better: I decided not to finish the book. ... Read more

Product Description
Information Theory and Statistics: A Tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an "information geometry" background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory. The tutorial does not assume the reader has an in-depth knowledge of Information Theoryor statistics. As such, Information Theory and Statistics: A Tutorial, is an excellent introductory text to this highly-important topic in mathematics, computer science and electrical engineering. It provides both students and researchers with an invaluable resource to quickly get up to speed in the field. ... Read more

Product Description

This book has been written for undergraduate and graduate students in various areas of mathematics and its applications. It is for students who are willing to get acquainted with Bayesian approach to computational science but not necessarily to go through the full immersion into the statistical analysis. It has also been written for researchers working in areas where mathematical and statistical modeling are of central importance, such as biology and engineering.

Product Description

Stochastic Tools in Mathematics and Science is an introductory book on probability-based modeling. It covers basic stochastic tools used in physics, chemistry, engineering and the life sciences. The topics covered include conditional expectations, stochastic processes, Brownian motion and its relation to partial differential equations, Langevin equations, the Liouville and Fokker-Planck equations, as well as Markov chain Monte Carlo algorithms, renormalization and dimensional reduction, and basic equilibrium and non-equilibrium statistical mechanics. The applications include data assimilation, prediction from partial data, spectral analysis, and turbulence. A noteworthy feature of the book is the systematic analysis of memory effects. The presentation is mathematically attractive, and should form a useful bridge between the theoretical treatments familiar to mathematical specialists and the more practical questions raised by specific applications.

The book is based on lecture notes from a class that has attracted graduate and advanced undergraduate students from mathematics and from many other science departments at the University of California, Berkeley. Each chapter is followed by exercises. The book will be useful for scientists and engineers working in a wide range of fields and applications.

Customer Reviews (3)

Fails half way between the math and the application
I am sad to give this book only three stars as I am a fan of one of its authors, Prof. Chorin, and have read many of his articles on numerical methods in fluid dynamics. But... after having owned this book for some time now have having given it "my bedside attention"! I must conclude that this book fails what it tries to achieve. It tries to introduce mathematical stochastic tools without the rigor to a "broader" applied mathematician audience. But the feeling I get is the "mathematician" part of the authors cannot let go and the book still has too much rigor, and the scope of presentation still remains too constrained, not opening up the subject enough for its application. And so my three start only rating!

A great "user's guide" to stochastic calculus and its important applications
This is an excellent user's guide to stochastic calculus and its important applications.I say excellent because it is well-written (clear and easy to read with a focus on the essentials as previous review said).I say "user's guide" because the author's get down to the nut's and bolt's of stochastic processes (concepts and related equations).I say "important applications" because they again focus on key "tools" such as Fokker-Planck equation and related stochastic differential equation, statistical mechanics, MCMC, turbulence, etc. - so there is something for everyone here e.g. physicists, financial "engineers", ME's and Aero's simulating turbulence (especially via PDF methods), etc.A few criticisms would be lack of "pixels" - they say a picture tell's a thousand words for a reason, hard to locate a term or definition or concept from index,...Overall, a very nicely priced/sized read and a must have reference for anyone working with stochastic processes seeking insight.

The Essentials
This book was written to fill in the gaps between what people learn in standard "pure" math trainings and what people use "in the field".The professors who wrote it come from a University where one can study math and never see topics from statistical sciences.They have attempted a very readable and clear exposition of some of the fundamentals behind the useful stochastic analysis in physics and economics.They use some formalism but largely skip long, difficult proofs, focusing instead on important examples.What I mean is they use the language of formal probability theory (measure theory), but keep the applications very concrete (differential equations, stochastic walks, etc).It cuts to the chase and is an exciting read for those like myself who get very turned off by overly formal probability theory, yet who are curious about some of the formalism.Short, paperback, good references, lively read. ... Read more

Product Description

This book is a complete treatise on the theory of nonlinear dynamics of chaotic and stochastic systems. It contains both an exhaustive introduction to the subject as well as a detailed discussion of fundamental problems and research results in a field to which the authors have made important contributions themselves. Despite the unified presentation of the subject, care has been taken to present the material in largely self-contained chapters.
The present book can thus be used either as a textbook by graduate students or as a modern monograph by researchers in this field.

The second edition has been substantially enlarged to include sections on statistical properties of dynamical chaos, on effects of synchronization in oscillatory systems and on synchronization in living systems.

Product Description

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis).

Product Description
The Tutorials in Biostatistics have become a very popular feature of the prestigious Wiley journal, Statistics in Medicine (SIM). The introductory style and practical focus make them accessible to a wide audience including medical practitioners with limited statistical knowledge.   This book represents the first of two volumes presenting the best tutorials published in SIM, focusing on statistical methods in clinical studies. Topics include the design and analysis of clinical trials, epidemiology, survival analysis, and data monitoring. Each tutorial is focused on a medical problem, has been fully peer-reviewed and edited, and is authored by leading researchers in biostatistics.   Many articles include an appendix on the latest developments since publication in the journal and additional references.

This will appeal to statisticians working in medical research, as well as statistically-minded clinicians, biologists, epidemiologists and geneticists. It will also appeal to graduate students of biostatistics. ... Read more

Product Description
The Tutorials in Biostatistics have become a very popular feature of the prestigious Wiley journal, Statistics in Medicine (SIM). The introductory style and practical focus make them accessible to a wide audience including medical practitioners with limited statistical knowledge.   This book represents the second of two volumes presenting the best tutorials published in SIM, focusing on statistical modeling of complex data. Topics include clustered data, hierarchical models, mixed models, genetic modeling, and meta-analysis. Each tutorial is focused on a medical problem, has been fully peer-reviewed and edited, and is authored by leading researchers in biostatistics.   Many articles include an appendix on the latest developments since publication in the journal and additional references.

This will appeal to statisticians working in medical research, as well as statistically-minded clinicians, biologists, epidemiologists and geneticists. It will also appeal to graduate students of biostatistics.  ... Read more

Product Description
The workbook and software diskette are intended to supplement the exercises in Statistics:Concepts and Applications.The programs provide a set of algorithms for standard computational statistical data analysis, and the workbook takes students step by step through the analytical techniques, so they gain a deeper understanding of statistical methods and can actually practice statistics in action. ... Read more