Showing posts with label r programming language. Show all posts
Showing posts with label r programming language. Show all posts

Global Sensitivity Analysis: The Primer Review

Global Sensitivity Analysis: The Primer
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Global Sensitivity Analysis: The Primer ReviewWhen I was working at Oak Ridge National Lab in the late 1970s, I worked with other scientists and statisticians on data and model validation. Toby Mitchell a specialist in experimental design was developing sampling techniques to use in model validation. He has since passed on. One of the techniques he used was Latin hypercube sampling. The authors of this text are from Italy and Canada. They have computer science and mathematical backgrounds but are not statisticians. Yet once you start reading book you will see that they appreciate both the deterministic and the stochastic aspects of modeling.
What they do, they call Global Sensitivity Analysis. They are very bright and are lucid in their explanations and description of philosophical issues. This is not something that those of us who do statistical modeling are very familiar with but it is important to know. It is especially gratifying to see that these authors are always wary of modeling assumptions and look for novel ways to test them. They point out that validating models is complicated. Many models that we construct are complex and even when we are aware of this and test aspects of the model. we often take some things for granted and accept aspects of the model as a given. I really enjoyed reading the afterword where these issues are well brought out.
Specific methods include th elementary effects method discussed in Chapter 3 that is based on the work of Max Morris in a 1991 paper in Technometrics. In Chapter 4, they cover variance-based methods which relies on the work of Sobol and others. They illustrate the applications of these methods with an infection dynamics model.
The main idea is to determine which factors affect the output variables as well as which interaction effects play a role. In the last chapter the authors make recommendations as to when to apply each technique.
At the beginning of the sections they raise questions that they answer in the section. This approach and the problem sets followed by complete and clear solutions makes the text readable and enjoyable even for novices.Global Sensitivity Analysis: The Primer Overview

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In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation Review

In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation
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In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation ReviewThe Traveling Salesman Problem is remarkable easy to state: given a set of points with distances between them, find the shortest tour through all the points. Despite its simple statement, the problem of finding good or optimal tours has generated thousands of research papers. The TSP is the standard testbed for discrete optimization: practically every possible approach to optimization can be tried out on the TSP. So research on the TSP has had a tremendous effect on approaches for all sorts of other optimization problems. This paper covers every aspect of this research corpus.
The author is the world's expert on this topic, and is clearly enraptured with this problem. His affection shows through his naming of his favorite heuristic, favorite computational model, and more. The book is wide-ranging and thorough and is written in a consistently engaging, conversational style, even when going very deep into the research literature.
This is not a research book, but it is a book about research. Through the TSP, Cook describes how research is done in a wide variety of subfields. The book requires some concentration but is accessible to any mathematically-inclined college student or bright high school student. For many, it will inspire further interest in algorithms and research.In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation Overview

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