Scientific Visualization: The Visual Extraction of Knowledge from Data (Mathematics and Visualization) Review

Average Reviews:

(More customer reviews)The many colour illustrations in the book are one of its neatest features. They help greatly in conveying what can be done with good visualisation ideas.
You might usefully combine reading this book with another recent text from Springer - "Visualising the Semantic Web" by Geroimenko and Chen. The latter book has more emphasis on XML encoded data that is geared towards the Semantic Web. Whereas this book takes a more general approach towards the data being researched. But the combination of both books may give insight into your visualisation issues.
The book covers a wide range of subjects. One section deals with volume visualisation in the medical field. Typically, there is medical data that comes from or maps to a 3 dimensional volume inside a body. How then to usefully display this?
While another section involves displaying vector fields. There is even a chapter delving into grid computing and how to handle the massive amounts of data generated.

Click Here to see more reviews about: Scientific Visualization: The Visual Extraction of Knowledge from Data (Mathematics and Visualization)

One of the greatest scientific challenges of the 21st century is how to master, organize and extract useful knowledge from the overwhelming flow of information made available by today's data acquisition systems and computing resources. Visualization is the premium means of taking up this challenge. This book is based on selected lectures given by leading experts in scientific visualization during a workshop held at Schloss Dagstuhl, Germany. Topics include user issues in visualization, large data visualization, unstructured mesh processing for visualization, volumetric visualization, flow visualization, medical visualization and visualization systems. The book contains more than 350 color illustrations.

Get 21% OFF

Click here for more information about Scientific Visualization: The Visual Extraction of Knowledge from Data (Mathematics and Visualization)

Read More...

The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series) Review

Average Reviews:

(More customer reviews)With the increase in cancer trials both in medical research and the pharmaceutical industry, medical diagnostic tests including medical imaging evaluations are being used more and more. There is a need for adjudication when radiologists disagree on a diagnosis and some standard statistical measures from other fields are finding new application.
This book provides a thorough background on the subject, the methodology and the applications. It is very clearly written and not overly technical. The methodology is the classical frequentist approach to statistical inference. Recently, Lyle Broemeling has published a book on this topic that takes exclusively the Bayesian approach and explains the Bayesian approach for those who are not acquainted with it. When dealing with predictions based on evolving data the Bayesian approach might be more natural. This would be the case in a clinical trial where the data is reviewed sequentially.
A particularly important part of diagnostic accuracy analysis is the area under the ROC curve. Pepe does an excellent job of covering that topic. This text is referenced often in Broemeling's book since it provides an excellent explanation of the same topics using the frequentist approach that he covers with the Bayesian approach.

Click Here to see more reviews about: The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series)

This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.

Click here for more information about The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series)

Read More...