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Scientific Reasoning: The Bayesian Approach Hardcover – October 12, 1999

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Product Details

  • Hardcover: 488 pages
  • Publisher: Open Court; 2 edition (October 12, 1999)
  • Language: English
  • ISBN-10: 0812692349
  • ISBN-13: 978-0812692341
  • Product Dimensions: 1.2 x 6.2 x 9.2 inches
  • Shipping Weight: 2.1 pounds
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #3,720,321 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

57 of 59 people found the following review helpful By Todd I. Stark VINE VOICE on July 31, 2002
Format: Paperback
This book is a little-known treasure in the philosophy of science that deserves a spot alongside the better known works of Popper, Kuhn, Lakatos, and Feyerabend, and is more practical than most of those. Herein lies the clearest, simplest, and most persuasive discussion I've ever seen on the limits of Karl Popper's view of science, along with a sound introduction to the Bayesian probability theory requiring no more than high school algebra and a little persistence.
Much of this book will strike students of classical probability theory and philosophy of science as very counter-intuitive at first, but it is so well argued and so clear that I think most readers will begin to warm up to the Bayesian view at least to some degree by the time they finish the book.
The book starts out introducing one version of the traditional "problem of induction": 'how can we be certain of a rule inferred from finite individual observations ?' We then quickly discover why the usual solutions offered don't quite work in actual theory construction in practice. Mainly, the usual solutions (generally based on the disconfirmation of hypotheses) don't address the way _auxilliary_ hypotheses help theories escape refutation, and how webs of evidence of different kinds often converge to help confirm theories.
It has been generally accepted by modern philosophers of science that useful scientific theories go well beyond the experimental data. Hence they can technically not be "proven" in a logical sense, only considered increasingly more likely as their testable predictions are validated.
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10 of 13 people found the following review helpful By Midwest Book Review on July 9, 2006
Format: Paperback
Now in its third edition, Scientific Reasoning: The Bayesian Approach, is a basic introduction to the philosophy that scientific reasoning is, and should be, conducted in accordance with the axioms of probability. Called the Bayesian view, after a theorem first proven by Thomas Bayes in the late eighteenth century, has recently gained increased standing as a valuable methodology for examining scientific evidence. Scientific Reasoning explains the elements of probability calculus that are relevant to Bayesian methods and argues that probability calculus should be understood as a form of logic. Accessibly written, even to readers who understand only the basics of probability or calculus, Scientific Reasoning is a solid explanation of how Bayesian theory offers a unified and highly satisfactory accounting of scientific procedure, and is an absolute "must-read" for all scientists and students of science.
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22 of 32 people found the following review helpful By Randall Helzerman on October 20, 2004
Format: Paperback
This book contains lots of useful information for the budding Baysian. Excellent discussions on many topics. However, I have to give this only 3 stars, because on a cardinal point, the authors give very bad advice: they give the impression that Komogorov complexity-based methods are ill motivated. In fact, Kolmogorov complexity is one of the most fruitful new developments in Baysianism, and I have personally used it many times in industrial settings to solve otherwise intractible problems.

However, on most points the book is very useful. I recommend buying the first edition over the second, because the second edition doesn't really add that much useful info over the first. I also recommend buying in addition to this book Ming Li and Paul Vianyi's book on Kolmogorov complexity, for a comprehensive intro to a whole wonderland of Baysianism which Howson & Urbach have overlooked.
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Format: Paperback Verified Purchase
If you have good command of elementary statistics, this is a good
first book for someone who is interested in practical uncertainty
quantification, that would like to learn about the Big Picture.
It is a book about thinking and working like a Bayesian, rather
than about techniques of Bayesian estimation.
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Format: Paperback
Howson and Urbach are totally committed to the subjectivist,Bayesian approach to probability founded by Ramsey,De Finetti,and Savage.This approach is based on the misbelief that all probability estimates are precise,exact ,unique single numbers.The decision maker can initially hold any subjective belief he wants to entertain as long as he is willing to incorporate additional evidence over time using Bayesian updating.This means that the decision maker uses the mathematical laws of the probability calculus (the addition and multiplication rules)to update his subjective probabilities so that they are consistent over time with the mathematical laws of the probablity calculus.His beliefs will be coherent and not subject to having a Dutch book made against him.Whatever his initial a priori ,subjective estimates of the probabilities were,the updated versions will start to converge to the correct a posteriori probabilities.The allegiance to the use of the mathematical laws of the probability calculus guarantee a decision maker that over time his assessments will be consistent.

The problems with this approach are based on the unspecified assumptions that there is always sufficient evidence to specify a sample space or unique probability distribution.This requires that there be NO uncertainty,vagueness,unclearness,ambiguity,or conflict in the evidence used to specify the probability distribution.Keynes specifies this by saying that the weight of the evidence is complete or equal to 1 on the unit interval [0,1].Ellsberg would specify this by requiring that rho =1,where rho is specified on the unit interval[0,1].Urbach and Howson simply assume that there will always be sufficient information that is clear in the present or future so that they can specify their distribution.
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