Data Analysis: A Bayesian Tutorial (Oxford Science Publications)

Book Description

Statistics lectures have often been viewed with trepidation by engineering and science students taking an ancillary course in this subject. Whereas there are many texts showing "how" statistical methods are applied, few provide a clear explanation for non-statisticians of how the principles of data analysis can be based on probability theory. Data Analysis: A Bayesian Tutorial provides such a text, putting emphasis as much on understanding "why" and "when" certain statistical procedures should be used as "how". This difference in approach makes the text ideal 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. With its central emphasis on a few fundamental rules, this book takes the mystery out of statistics by providing a clear rationale for some of the most widely-used procedures.

Customer Reviews:

concise but clear.......2006-03-06

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........2005-12-05

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.......2004-10-02

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 concise explanations. 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".......2004-09-15

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.......2004-01-17

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.

Likelihood, Bayesian and MCMC Methods in Quantitative Genetics

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Highly recommended!

Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
Daniel Sorensen
Manufacturer: Springer
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Binding: Hardcover

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ASIN: 0387954406

Book Description

Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently. This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience. The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a Bayesian perspective. An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments. Daniel Sorensen is a Research Professor in Statistical Genetics, at the Department of Animal Breeding and Genetics in the Danish Institute of Agricultural Sciences. Daniel Gianola is Professor in the Animal Sciences, Biostatistics and Medical Informatics, and Dairy Science Departments of the University of Wisconsin-Madison. Gianola and Sorensen pioneered the introduction of Bayesian and MCMC methods in animal breeding. The authors have published and lectured extensively in applications of statistics to quantitative genetics.

Customer Reviews:

Highly recommended!.......2004-08-27

This book contains a wealth of well presented and organized information, which is not easy to find in texts of similar level. I especially enjoyed the style and clarity of presentation. Outstanding!

Applied Bayesian Forecasting and Time Series Analysis (Chapman & Hall Texts in Statistical Science)

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A good introduction to dinamic models
applied bayesian forecasting and time series analysis

Applied Bayesian Forecasting and Time Series Analysis (Chapman & Hall Texts in Statistical Science)
Andy Pole , Mike West , and Jeff Harrison
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

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ASIN: 0412044013

Book Description

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Â· Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Â· Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Â· Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.

Customer Reviews:

A good introduction to dinamic models.......2001-06-25

This is a hands-on introduction to bayesian forecasting and dinamic models. After a brief overview on theory, it leads you to some fully developed examples. The second part of the book simply develop some examples on the BATS program supplied with the book. They clearly feature the main capabilities of the program. The main drawback is that all the book is focused on that program, so BF and DM are glossed over. So, a reading to the theory focused book "bayesian forecasting and dinamic models" from Harrison is mandatory for a deeper dive into this issue.

applied bayesian forecasting and time series analysis.......2000-03-31

Pole et al.'s small primer on bayesian time series analysis is a good first step for an outsider to the area. The book is split into two parts. The first gives a favourable treatment of bayesian analysis. The second half of the book is an extended tutorial to accompany the canned program and data set included with the text.

Book Description

This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian methods for analyzing real-world problems in the health sciences. The work considers the individual components of Bayesian analysis.;College or university bookstores may order five or more copies at a special student price, available on request from Marcel Dekker, Inc.

Customer Reviews:

Pricey, but quicker than collecting articles by yourself.......2000-04-04

Perhaps more appropriately titled _Examples in Bayesian Biostatistics_, this book is organized as a series of articles on the use of Bayesian methods in a variety of biostatistical settings. The benefit of this approach is that each article is written by experts who provide their own insights to the methodology presented. The downside is that each article is written by experts, who use slightly different notation, and exhibit varying degrees of ability to communicate their results (they are on the whole fairly good).

Probability, Random Signals, And Statistics: A Textgraph with Integrated Software for Electrical & Computer Engineers

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Not recommended for beginners
A pleasant surprise
Excellent text for probability theory

Probability, Random Signals, And Statistics: A Textgraph with Integrated Software for Electrical & Computer Engineers
X. Rong Li
Manufacturer: CRC Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0849304334

Book Description

With this innovative text, the study-and teaching- of probability and random signals becomes simpler, more streamlined, and more effective. Its unique "textgraph" format makes it both student-friendly and instructor-friendly. Pages with a larger typeface form a concise text for basic topics and make ideal transparencies; pages with smaller type provide more detailed explanations and more advanced material.

Customer Reviews:

Not recommended for beginners.......2002-03-24

I am afraid I can't wholly recommend this text for people taking a first look at probabilities, especially if they are studying independently. The major problem is Li's lack of clarity and explanation. He just seems unnecessarily confusing and abstract. He does state essential points right up front, but not enough attempt is made to provide intuitive or common-sense explanations. Examples are mostly derivations of special cases. More useful examples are provided at the end of chapters, but they would be better if integrated with the text. Readers experienced with the subject might appreciate Dr. Li's terseness, but for beginners I would more highly recommend "The Probability Tutoring Book" by Ash.

A pleasant surprise.......2001-12-28

While the literature on probability and random signals is enormous, college classrooms have long been dominated by a few classics. New books attempting to compete in this area haven't seen much success in recent years.

As a college instructor, I find this new addition a really pleasant surprise. The style certainly is a notch above many other texts, yet the more important feature to me is the way theories are presented. While mathematical rigor is not compromised, a clear emphasis on fundamental principles and intuitive thinking has its great appeal, especially to students who have not had much experience with random variables etc. As claimed by the author, it is ``extremely instructor-friendly'' because of its Textgraph format. Indeed, the clever mixture of `viewgraph' with the in depth discussion in the book makes it very appealing for classroom use.

Another feature is the carefully compiled problems including self-test problems that should be well received by motivated students. Reading chapter 5 has also been a quite enjoyable experience as it summarizes succinctly major applications of probability theory before it gets to the more advanced topics on random processes. The accompanied user-friendly software is also a plus --- being able to play with various probability concepts will certainly enhance greatly the students' understanding of the subject matter.

Excellent text for probability theory.......2000-04-18

I picked up this book as a reference book to complement the probability intro book and digital communications, dsp books I have. This book provides readers a good grasp of the basic concept of random variables, linear systems (incl. matched filter, wiener filter ..)The main advantage of this book is the presentation method(textgraph -like well-typed lecture notes which is a privilege for me). Excellent explanation (concise and well organized). Historical/interesting anedotes too.

The Statistical Theory of Shape (Springer Series in Statistics)

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The Statistical Theory of Shape (Springer Series in Statistics)
Christopher G. Small
Manufacturer: Springer
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Binding: Hardcover

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ASIN: 0387947299

Book Description

The shape of a data set can be defined as the total of all information under translations, rotations, and scale changes to the data. Over the last decade, shape analysis has emerged as a promising new field of statistics with applications to morphometrics, pattern recognition, archaeology, and other disciplines. This book provides a comprehensive coverage of the statistical theory of shape. Both the Kendall and the Bookstein schools of shape analysis are described. It is written for graduate students and researchers in statistics who have some knowledge of multivariate models. An understanding of the basic concepts of differential manifolds is also helpful.

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First "hands on" sensitivity analysis
Sensitivity analysis for everybody

Sensitivity Analysis
A. Saltelli
Manufacturer: John Wiley & Sons
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Binding: Hardcover

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ASIN: 0471998923

Book Description

Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice, and is an implicit part of any modelling field.

Â· Offers an accessible introduction to sensitivity analysis

Â· Covers all the latest research

Â· Illustrates concepts with numerous examples, applications and case studies

Â· Includes contributions form the leading researchers active in developing strategies for sensitivity analysis

The principles of sensitivity analysis area carefully described, and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire causal assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A 'hitch-hiker's guide' is included to allow the more experienced reader to readily access specific applications.

Modellers from a wide range of disciplines, including biostatistics, economics, environmental impact assessment, chemistry and engineering will benefit greatly form the numerous examples and applications.

Customer Reviews:

First "hands on" sensitivity analysis.......2007-07-02

This book provides introduction to theoretical background and first "hands on" practical computations in sensitivity analysis. Sensitivity analysis is based on systems view of statistical evaluation in quantification and extraction of effects of input factors in multivariate, nonlinear and dynamical systems on system output performance. The authors review essentials of theoretical approaches and algorithms through several simple small scale problems with paradigm notions. At the same time, the book serves as a guide for use of freely available software SimLab provided by the same authors.
The book is highly recommendable to all researchers involved in modeling in a very broad range of complex systems, such as in economy, environmental studies, biology, medical engineering, chemistry etc.

Sensitivity analysis for everybody.......2001-01-29

Too often modellers do not realise that sensitivity analysis is an essential part of the model building process. This volume has a didactical value showing how SA is often useful - and sometimes essential- to complete the model building process and to interpret results properly. It guides the reader through an array of different approaches, illustrating in a generally clear fashion the specificity of the different techniques to different problem-setting.

Although this is a multi-authored book, the discourse flows clearly across (most of) the chapters and coveys the main element of this new discipline.

The authors-editors show an overall preference for sensitivity analysis methods capable of global quantitative sensitivity analysis; the sections of the book devoted to local methods and to regression analysis are rather a useful review than actually new material. The sections on variance-based methods and on high dimensional model representations are probably the most instructive for the educated reader.

Book Description

This volume compiles geostatistical and spatial autoregressive data analyses involving georeferenced socioeconomic, natural resources, agricultural, pollution, and epidemiological variables. Benchmark analyses are followed by analyses of readily available data sets, emphasizing parallels between geostatistical and spatial autoregressive findings. Both SAS and SPSS code are presented for implementation purposes. This informative casebook will serve geographers, regional scientists, applied spatial statisticians, and spatial scientists from across disciplines.

Percolation (Grundlehren der mathematischen Wissenschaften)

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Excellent
Percolation

Percolation (Grundlehren der mathematischen Wissenschaften)
Geoffrey Grimmett
Manufacturer: Springer
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Binding: Hardcover

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ASIN: 3540649026

Book Description

Percolation theory is the study of an idealized random medium in two or more dimensions. The mathematical theory is mature, and continues to give rise to problems of special beauty and difficulty. Percolation is pivotal for studying more complex physical systems exhibiting phase transitions. The emphasis of this book is upon core mathematical material and the presentation of the shortest and most accessible proofs. The book is intended for graduate students and researchers in probability and mathematical physics. Almost no specialist knowledge is assumed. Much new material appears in this second edition, including: dynamic and static renormalization, strict inequalities between critical points, a sketch of the lace expansion, and several essays on related fields and applications.

Customer Reviews:

Excellent.......2000-04-26

The latest edition of Dr Grimmett's Percolation is surely the best book on the subject. He presents topics as clearly as possible without neglecting the technical details. His writing style is very readable, making much of this book accessible even to those who don't have all the necessary background in mathematics to understand all the proofs. Anyone looking for an easy introduction to the topic would be better off with Stauffer's book. But to gain any moderate understanding of this fascinating subject, and the methods and results of current research, this is the only book to have.

Percolation.......2000-04-05

Grimmett's book, Percolation, is excellent.

Percolation theory began in the 50's; its mathematics is now quite mature, but the theory has recently acquired new techniques because many of the questions initially raised by percolation theory are still unanswered.

Percolation technology is now a cornerstone of the theory of disordered systems, and the methods of this book are now being extended into dynamical systems theory and the life sciences. This book covers the mathematics of percolation theory, presenting the shortest rigorous proofs of the main facts. Many problems in percolation theory are beautiful, but some of the apparent simplicity of the subject is deceiving, because the subject is quite deep. Grimmett cuts through many of the difficulties presenting the important concepts clearly and sucinctly.

The author restricts himself- for accessibility to the maximum readership-to bond percolation on a cubic lattice. Grimmett presents the core material at a graduate level for folks conversant with elementary probability theory and real analysis. Having some knowledge of ergodic theory, graph theory, and some mathematical physics helps, however. There is litle discussion of continuous, mixed, inhomogenous, long range, first passage or oriented percolation.

Beginning with existance of Psubc for the edge probability p we arrive at an infinite open cluster followed by discusssion of the basic techniques of the FKC, BK inequalities and Russo's formula. Grimmett then discusses open clusters per vertex and subcritical percolation, beginning with the Aizeman-Barsky and Menshikov methods for identifying the critical point, followed by a systematic study of the subcritical phase. He then discusses supercritical percolation, including 2 dimensional percolation, continuum percolation and random processes. The author gives a full list of references.

Average customer rating:

excellent topic, well covered, with software for implementation

Adaptive Design Theory and Implementation using SAS and R (Biostatistics: A Series of References and Textbooks)
Mark Chang
Manufacturer: Chapman & Hall/CRC
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Adaptive Design Methods in Clinical Trials (Biostatistics: A Series of References and Textbooks)

Statistical Monitoring of Clinical Trials: A Unified Approach (STATISTICS FOR BIOLOGY AND HEALTH)

Sample Size Calculations in Clinical Research (Biostatistics, 11)

Bayesian Approaches to Clinical Trials and Health Care: Statistics in Practice

Pharmaceutical Statistics Using SAS: A Practical Guide (SAS Press)

ASIN: 1584889624

Book Description

Adaptive design has become an important tool in modern pharmaceutical research and development. Compared to a classic trial design with static features, an adaptive design allows for the modification of the characteristics of ongoing trials based on cumulative information. Adaptive designs increase the probability of success, reduce costs and the time to market, and promote accurate drug delivery to patients. Reflecting the state of the art in adaptive design approaches, Adaptive Design Theory and Implementation Using SAS and R provides a concise, unified presentation of adaptive design theories, uses SAS and R for the design and simulation of adaptive trials, and illustrates how to master different adaptive designs through real-world examples. The book focuses on simple two-stage adaptive designs with sample size re-estimation before moving on to explore more challenging designs and issues that include drop-loser, adaptive dose-funding, biomarker-adaptive, multiple-endpoint adaptive, response-adaptive randomization, and Bayesian adaptive designs. In many of the chapters, the author compares methods and provides practical examples of the designs, including those used in oncology, cardiovascular, and inflammation trials. Equipped with the knowledge of adaptive design presented in this book, you will be able to improve the efficiency of your trial design, thereby reducing the time and cost of drug development.

Customer Reviews:

excellent topic, well covered, with software for implementation.......2007-07-18

This book just came out but I know a lot about it and about the author before I even got a copy. In November of last year Mark Chang coauthored a book in this Chapman and Hall series that I reviewed with praise because of the importance of the topic and the way it was demonstrated to work in a variety of real problems in pharmaceutical clinical trials. This book is even better as it goes more deeply into the methodology, the controversies and the results from simulation studies. Also it is much more practical because for every case where an application is given a SAS macro is also included to allow the reader to try the methodology for himself. In March of 2007 I actually designed a two-stage adaptive design with sample size reestimation for bioequivalence trials. I met mark at a conference where he presented much of his recent work and he was instrumental in helping me through his first book and his journal articles. This book had already gone to the publisher but he realized that this important design had overlooked. He added it when the copyedited version came to him. The design and the simulations related to it are very close to what I actually used. For those who like to program in R, he provides R code corresponding to each of the SAS macros that he gave. These programs make the new methodology readily available to interested users. The book is very comprehensive in that it covers a wide variety of applications for phase 2, phase 3 and combined phase trials. With the FDAs new initiative to speed up the drug discovery process this book will be an invaluable tool to statisticians in the pharmaceutical industry who would like to learn and apply these methods that along with the group sequential methodsare gaining favor within the FDA.

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