Book Description
Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples.
Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances.
- Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results.
- Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison.
- Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations.
- Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology.
- Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets.
Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.
Customer Reviews:
This is the book you want.......2007-06-07
Bayesian methods are moving into structural equation modeling. The most sophisticated approach to modeling interactions is Bayesian. People who want to be able to predict the values of observed variables need a Bayesian approach.
This book, with the code and datasets available from the publisher's website, will help you to estimate SE models using the Bayesian approach and the free WinBUGS software. Yes, it's a math-heavy book, but Sik-Yum Lee does a great job explaining this very different approach. Lee demonstrates Bayesian methods applied to basic models, interaction models, mixture models, multi-level models, and models with non-normal distributions. You really want to have this book, if you are a serious SEM user.
Structural Equation Modelling: A Bayesian Approach.......2007-05-07
I would rather not recommend this book to whom is looking for SEM. This book is more like Math oriented..so it is difficult to mention that it is good for students seeking for answeres from the business or sociological perspectives.
Book Description
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.
Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.
The second edition:
- Provides an integrated presentation of theory, examples, applications and computer algorithms.
- Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
- Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.
- Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.
- Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs.
Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.
Praise for the First Edition:
“It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.”
– ISI - Short Book Reviews
“This is an excellent introductory book on Bayesian modelling techniques and data analysis”
– Biometrics
“The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.”
– Journal of Mathematical Psychology
Customer Reviews:
Use it against spam?.......2004-02-24
One specific application of Bayesian approaches has recently become hot. It has been claimed by some that a way to attack spam in email is to use Bayesian filters. If that is your inclination, you may want to check out this book.
It has a solid, technical explanation of Bayesians and is replete with several examples. Some parts may be heavy going, depending on your mathematical background. But worth it eventually.
The problem is, if you do develop a Bayesian filter for spam and use it, you may find that there are fundamental limitations, due to broadening, and to spammers actively counterattacking Bayesians.
The examples in the book of applications all involve cases where the system being modelled does not change significantly; otherwise the Bayesian will have to be retrained on new data. But, and more importantly, the examples do not treat the case where the system can change in a way to deliberately defeat the Bayesian.
nice recent text on Bayesian methods with many applications.......2001-12-26
Congdon presents a very nice and modern treatment of Bayesian methods and models emphasizing implementation using BUGS or WINBUGS. The book covers Bayesian models for regression including linear, log-linear, robust and nonparametric regression. Covers association and classification, mixture models, latent variables, problems of missing data, survival analysis, hierarchical models for pooling information, time series and other correlated data methods (e.g. spatial processes), multivariate analysis, growth curves and model assessment criteria.
The book is loaded with techniques and applications covering a wide variety of topics with reasonable depth.
It also has a very large bibliography with many very relevant and useful references. But there is also a negative side to the bibliography. It was not carefully proofread and there are some annoyances as you will see the same reference listed two, three or more times in the bibliography. Also for such a nice reference text it should have included an author index as well as an ordinary index.
Gibbs sampling is one of the primary estimation techniques in the book but the details are put off until section 10.1 where we get a nice introduction to Gibbs sampling and also the Metropolis algorithm with several excellent references.
This is a good book to start implementing Bayesian methods through the MCMC technique. It contains mostly medical applications which is a nice feature for biostatisticians.
Book Description
The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book
· Provides a broad and comprehensive account of applied Bayesian modelling.
· Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications.
· Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology.
· Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site.
The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.
Download Description
"The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book · Provides a broad and comprehensive account of applied Bayesian modelling. · Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications. · Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology. · Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site. The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis."
Book Description
New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.
Book Description
This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required.
The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added. This second edition comes with a CD-ROM by F. Friedrich,containing a host of (live) illustrations for each chapter. In an interactive environment, readers can perform their own experiments to consolidate the subject.
Customer Reviews:
a bible book to learn Gibbs sampler and simulated annealing.......2000-07-12
This is absolute a bible book for any person who want to learn Gibbs sampler and simulated annealing seriously. The format of this book, though full of mathematical equations, is very self-evident and concise. Nothing is missing and nothing is redundent. It is an enjoyable journey to follow the logic and principle in this book, with all your attention in. There are full of in-depth discussion in all aspect of the Gibbs sampler, simulated annealing, from the visiting scheme to cooling schedule, and parallel algorithms. The references are excellent too. The author seems to have read all publications till 1995 about this topic and give an excellent detailed and in-depth survey in his book. At the end of your reading, you would have love the mathematical form the author used. Without these tools, many discussions in this book will be just impossible and groundless. I personally have read this book for several times.
Book Description
Newcomers to the world of probability face several potential stumbling blocks. They often struggle with key concepts-sample space, random variable, distribution, and expectation; they must regularly confront integration, infrequently mastered in calculus classes; and they must labor over lengthy, cumbersome calculations. Introduction to Probability with Mathematica is a groundbreaking text that uses a powerful computer algebra system as a pedagogical tool for learning and using probability. Its clever use of simulation to illustrate concepts and motivate important theorems gives it an important and unique place in the library of probability theory. The author smoothly integrates the technology with the traditional approach and subject matter, thereby augmenting rather than overpowering it. This book lives and breathes in the sense that not only can it be read and studied in an armchair, but each section also exists as a fully executable Mathematica® notebook on the CRC Web site. Students will find Introduction to Probability with Mathematica an engaging, accessible, yet challenging way to venture into the fascinating subject of probability.
Customer Reviews:
Sample Programs are Available.......2004-12-30
One reviewer said that the sample programs were not available as promised on the publishers website. That may have been true when that review was written, however, I just checked and the sample programs are now there for download on the publisher's website. In fact, I just downloaded them and they are fully functioning Mathematica notebooks.
An excellent book.......2003-12-12
I purchased this book in desperation while taking a probability class with another textbook, and it has been a lucky find, indeed. The mathematics are limited mostly to basic calculus but provide sufficient rigor to satisfy the interests of mathematically-minded readers. The concepts appeal intuitively to the non-statistician scientist or graduate student as well as the mathematician. This book is easy to read and understand. Mathematica enhances the text and aids the homework, but unlike the reviewer below, I believe this book is valuable even without Mathematica. After reading this book I was able to make sense of the assigned probability text and began to enjoy the course. Introduction to Probability with Mathematica was well worth the investment.
A creative and refreshing approach..........2003-11-05
I considered this book for a course that I thought I was going to teach. The course never got offered but I did discover this neat book in the process. Being a Mathematica fan, I was very happy to see a probability book completely based on Mathematica. In fact, the book itself is a set of Mathematica notebooks, making it very easy for the readers to experiment with the introduced topics. The explanations are clear and are accompanied with neat examples showing real-world uses of probability.
There should be more books like this... Really.
........
I was a guinea pig.......2001-01-09
I was one of the test users of this book at the college where Prof. Hastings teaches. With the use of Mathematica (which is assumed in this book), the book allows one to explore the ideas much more easily. By modifying built in commands, the user can get a better grasp on how specific distributions behave. The commands written for the book are also very helpful. I found the book easy to use and the problems ranged from basic to difficult, but most were intersting (particularly the chapter on simulation). For those with access to Mathematica, this book works seemlessly with the program, making Mathematica a simple to use tool to aid in the understanding of probability rather than get in the way.
No software available.......2000-12-27
The book seems to have good intentions.. but... most of the text uses Mathematica Code that is not available... the promised software on the publishers Website is apparently vaporware.. totally unavailable.. and of course the publishers are "not available"... Without software the book is an expensive waste of time...
Average customer rating:
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Probabilistic Modelling in Bioinformatics and Medical Informatics
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Consumer Health Informatics: Informing Consumers and Improving Health Care (Health Informatics)
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Aspects of Electronic Health Record Systems (Health Informatics)
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Medical Informatics: Knowledge Management and Data Mining in Biomedicine (Integrated Series in Information Systems)
ASIN: 1852337788 |
Book Description
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
Book Description
The first edition of Multivariate Statistical Modelling provided an extension of classical models for regression, time series, and longitudinal data to a much broader class including categorical data and smoothing concepts. Generalized linear modesl for univariate and multivariate analysis build the central concept, which for the modelling of complex data is widened to much more general modelling approaches. The primary aim of the new edition is to bring the book up-to-date and to reflect the major new developments over the past years. The authors give a detailed introductory survey of the subject based on the alaysis of real data drawn from a variety of subjects, including the biological sciences, economics, and the social sciences. Technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. The appendix serves as a reference or brief tutorial for the concepts of EM algorithm, numberical integration, MCMC and others. The topics covered inlude: Models for multi-categorial responses, model checking, semi- and nonparametric modelling, time series and longitudinal data, random effects models, state-space models, and survival analysis. In the new edition Bayesian concepts which are of growing importance in statistics are treated more extensively. The chapter on nonparametric and semiparametric generalized regression has been rewritten totally, random effects models now cover nonparametric maximum likelihood and fully Bayesian approaches, and state-space and hidden Markov models have been supplemented with an extension to models that can accommodate for spatial and spatiotemporal data. The authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, this book is ideally suited for applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis from econometrics, biometrics and the social sciences.
Customer Reviews:
Absolutely an excellent work. Don't hesitate to pay for it!.......2005-09-22
[1] Studying bioinformatics? You must be familiar with multivariate analysis. This book is absolutely an important reference.
[2] A researcher of statistical pattern recognition? Without doubt, you need this up-to-date book to stuff your toolbox.
multivariate methods using generalized linear models.......2001-05-11
Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.
I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.
The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.
Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.
Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).
A quality text.......2001-04-04
Great book! Presents clear information about statistical computational details, as well as a number of nonstandard models (including those of Tutz's original work). The book has a transparent build-up, from more easy modeling exercises to advanced applications. I like best the part on generalized linear time series modeling, using the extended Kalman filter in the context of the EM algorithm. The only critique I have concerns the handling of (the variance of) the measurement error term in the associated generalized state space model (this measurement error should be modeled as a constrained martingale difference).
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Advances in Probabilistic Graphical Models (Studies in Fuzziness and Soft Computing)
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ASIN: 354068994X |
Book Description
In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;
contributions to the area are coming from computer science, mathematics, statistics and engineering.
This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional
independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.
Book Description
This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.
Customer Reviews:
advanced text, good coverage, unique Bayesian perspective.......2002-04-26
This is a modern advanced text on econometrics emphasizing dynamic models including the ARCH/GARCH models that have practical application in finance. What makes it a little different than most texts is the Bayesian approach. The authors include coverage of MCMC methods which make the Bayesian approach more realistic. This book provides a very modern treatment of econometrics
Excellent introduction to Bayesian Time Series Econometrics.......2000-06-18
This is an outstanding introduction to the application of Bayesian statistics to the problems encountered in macroeconomics and finance. Bayesian inference it's becoming a critical tool for researchers and practitioners with an interest in empirical ecnomics and to date this is the first book on Bayesian time series econometrics. The sections on nonlinearities and on numerical integration are especially valuable. Having the book it is a must for researchers and professionals interested in modeling and forecasting the state of economy and financial markets.
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- System Reliability Theory: Models, Statistical Methods, and Applications, Second Edition
- The Art Student Survival Guide
- The Variational Principles of Mechanics (Dover Books on Physics and Chemistry)
- Tool and Object: A History and Philosophy of Category Theory (Science Networks. Historical Studies)
- Vector Calculus
- Viscous Fluid Flow (Mcgraw Hill Series in Mechanical Engineering)
- Welding: Principles and Applications, Fifth Edition
- What Went Wrong?, Fourth Edition: Case Studies of Process Plant Disasters
- A Biologist's Guide to Mathematical Modeling in Ecology and Evolution
- A Biologist's Guide to Mathematical Modeling in Ecology and Evolution
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