Concurrency: State Models and Java Programs Review

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(More customer reviews)This book does not belong with other Java books. This is a real computer science textbook that should be sitting next to Rivest's Algorithms book. The use of Java is incidental and makes the book very accessible without detracting from the issues at hand. It has to be one of the most well conceived computer science books ever written.
The material covered is maddeningly difficult to master informally because concurrency errors often manifest themselves as rare, random, and disastrous events that cannot be reproduced. Most of the book is laying down a solid formal foundation in which to reason about concurrency; the only hope for getting it right when things are difficult. It also comes with analysis tools (in Java of course!) to help people who learn by exploration, experimentation, and visualisation.

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Concurrency provides a thoroughly updated approach to the basic concepts and techniques behind concurrent programming. Concurrent programming is complex and demands a much more formal approach than sequential programming. In order to develop a thorough understanding of the topic Magee and Kramer present concepts, techniques and problems through a variety of forms: informal descriptions, illustrative examples, abstract models and concrete Java examples. These combine to provide problem patterns and associated solution techniques which enable students to recognise problems and arrive at solutions.
New features include:
New chapters covering program verification and logical properties.
More student exercises.
Supporting website contains an updated version of the LTSA tool for modelling concurrency, model animation, and model checking.
Website also includes the full set of state models, java examples, and demonstration programs and a comprehensive set of overhead slides for course presentation.

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Bayesian Methods for Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) Review

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(More customer reviews)I like this book a lot. It's not the book that I would've written, and that's a good thing. Buying Carlin and Louis along with our book will give you two perspectives on applied Bayesian statistics as it is practiced in the 21st century. Compared to our book, Carlin and Louis offer the following:
- Discussion of the debates over Bayesianism within the statistical community, culminating in chapter 5, which covers the links between Bayes, empirical Bayes, and frequentist methods of evaluating statistical procedures.
- A crisp presentation of Bayesian computation (chapter 5), which offers a different perspective than ours.
- A chapter on experimental design including several biomedical examples. This chapter should be useful to a lot of people, I think.
- Near the end of the book, discussion of several classes of models--longitudinal analysis, survival analysis, spatial models, clinical trials, and others--where I often think, "What's would a Bayesian do here?"
I don't think Carlin and Louis have made our own Bayesian Data Analysis obsolete but I do think their book is a great complement to ours, with a slightly different perspective, strong coverage of the theoretical issues of point and interval estimation, and a bunch of compelling biomedical examples.

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Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling-now commonplace in Bayesian data analysisA new chapter on Bayesian design that emphasizes Bayesian clinical trialsA completely revised and expanded section on ranking and histogram estimationA new case study on infectious disease modeling and the 1918 flu epidemicA solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem-available both electronically and in printIdeal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.

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Modeling With NLP Review

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(More customer reviews)I ordered this book last year and have read it many times over. Robert succeeds in s-p-e-l-l-i-n-g out not only how to model, but how to detect patterns and replicate them. Patterns both cognitive and/or physical. He provides a framework upon which to organize all of your resources (NLP and otherwise) to get exactly what you want. What if you looked at the world in a way that everything around you was yours for the *learning* such as things which we label as 'talents', 'athletic skill', or even 'IQ'? What if everyone is truly an example of excellence, and you had the key to replicate that excellence?
I recently went to Rex Sikes' outstanding Master Prac / Modelling Seminar. This book is a welcome addition to the material presented by Rex. For those who haven't signed up for his seminar yet, this is the best book on modelling I've found.
Through modelling, the world is your oyster.

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The hard cover version is not available any more.The paper back version is available.The ISBN# is0-916990-46-X. If you can not order this book from Amazon.com.Please contact us at metapub@prodigy.net.

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Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)I've bought several spatial statistics books over the years and found they generally fall into one of two categories; oversimplified or cover-to-cover matrix notation, neither of which is very useful for my research. However, this book is "just right," bridging these two extremes. It briefly covers the basics of both point and areal analysis, then gives the reader the tools for more advanced (i.e., realistic) analysis. They devote a chapter to Bayesian basics, which is needed for the last 4 or 5 chapters. The last few chapters weave together a detailed discussion on a variety of hierarchical models and current published results. Most importantly this book offers quite a bit of the necessary R and Winbugs code. Although many of their examples are from the public health world, the techniques and code are easily adapted to natural resource data - my personal focus.

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Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.This book provides clear explanations, plentiful illustrations --some in full color--a variety of homework problems, and tutorials and worked examples using some of the field's most popular software packages.. Written by a team of leaders in the field, it will undoubtedly remain the primary textbook and reference on the subject for years to come.

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