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Likelihood

Likelihood

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Author: A. W. F. Edwards
Publisher: The Johns Hopkins University Press
Category: Book

List Price: $25.00
Buy New: $23.44
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New (17) Used (7) from $14.00

Rating: 4.5 out of 5 stars 3 reviews
Sales Rank: 609181

Media: Paperback
Edition: Expanded
Pages: 296
Number Of Items: 1
Shipping Weight (lbs): 0.8
Dimensions (in): 7.9 x 5 x 0.6

ISBN: 0801844436
Dewey Decimal Number: 501.51954
EAN: 9780801844430

Publication Date: October 1, 1992
Availability: Usually ships in 1-2 business days
Shipping: International shipping available
Condition: Brand new book! Delivered direct from our US warehouse by Expedited (4-7 days) or Standard (usually 10-14 days but can be longer). Expedited shipping recommended for speedier delivery. Over 1 million satisfied customers

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Editorial Reviews:

Product Description

"The book is indeed a classic. Virtually every philosopher of science now writing about probabilistic inference has been influenced by Edwards' book, and his ideas are now as alive and relevant as they were when the book first appeared. Edwards is an absolutely seminal thinker in the foundations of statistics and scientific inference." -- Elliott Sober, University of Wisconsin-Madison.

"Full of appropriate examples (especially from genetics) and historical commentary, this monograph offers a rare simultaneous treatment of both mathematical and philosophical foundations." -- American Mathematical Monthly.

This new and expanded edition of A. W. F. Edwards' classic volume on scientific inference presents his most important published articles on the subject. Edwards argues that the appropriate axiomatic basis for inductive inference is not that of probability, with its addition axiom, but that of likelihood, the concept introduced by Fisher as a measure of relative support among different hypotheses. Starting from the simplest considerations and assuming no more than a basic acquaintancewith probability theory, the author sets out to reconstruct a consistent theory of statistical inference in science. Using the likelihood approach, he explores estimation, tests of significance, randomization, experimental design, and other statistical topics. Likelihood is important reading for students and professionals in biology, mathematical sciences, and philosophy.

"This book is commended to all philosophers of science who are interested in the problems of scientific inference." -- Search.

"This book, by a well-known geneticist, will do much to publicize the generality of the likelihood method as a foundation for statistical procedure. It is both smoothly written and persuasive." -- Operations Research.

"Likelihood is an important text and, in addition, is a joy to read, being a paragon of lucid and witty exposition." -- Mathematical Gazette




Customer Reviews:

5 out of 5 stars Fisher's Last Student At Caius College Cambridge   December 10, 2006
R. Prabhaharan (Singapore / Malaysia)
8 out of 9 found this review helpful

To those who work in academic probability theory, the name of the world famous Cambridge University statistician and theoretical geneticist Ronald Fisher needs no introduction, just as true genius needs no favourable introduction by friends who knew them. To those who work in practical statistics today, the method of maximum likelihood has become something of a cliche. But it was first invented by R Fisher and was an idea of pure genius. A W Edwards was Fisher's last PhD student supervised at the famous mathematical college Caius. He has extended Fisher's work and given it a new twist - to use ideas from mathematical logic to axiomatise the method of maximum likelihood and amazingly rigorously build up a new theory of probability, different from Laplace and Kolgomorov. Please buy this book - it will give you a new perspective of probability.


5 out of 5 stars a bit dated but a great little book, especially for the price   April 24, 2008
David Myers (Franklin, TN)
Owen's book "Empirical Likelihood" (2001) is the successor of the mantle of this book, and you may want to own both if you plan to do analysis in this arena. Likelihoodists are a rare little breed sitting on the fence in between Bayesians and Frequentists. There is so much muddling between the camps now that most recently graduated practicioners aren't likely (there's that word again) to draw a crisp distinction between the camps. But this volume does draw the philosophical argument out into the open, rawly. For those that say the arguments are settled, I'd say read this volume and at least absorb what it has to say. Then move on to the more updated (and more expensive) book by Owen. This book is less dogmatic than Earman's Bayes or Bust, though it does quote from Earman a fair bit. You'll appreciate the simplicity of the writing and style here, too. Edwards' last stand on this topic is worth the look.


4 out of 5 stars An interesting mix   January 14, 2004
wiredweird (Earth, or somewhere nearby)
13 out of 14 found this review helpful

Reasoning in terms of likelihoods is completely routine these days. In many areas - string matching, signal decoding, model-building - it is used without any special mention, like any other fundamental. It seems hard to remember that, not too long ago, likelihood and Bayesian techniques were the topics of impassioned debate - somtimes on different sides of the debate. This little book dates back to that era.

The book addresses the use of likelihood in a number of familiar applications (parameter estimation, etc). The examples are numerous and clear. I find more recent writings to be more directly applicable, though.

The real value of this book, for me, is the historical perspective that the author brings to the discussion. Early in his career, he worked with Fisher - one of the founders of modern statistics. Edwards describes how likelihood-based analysis grew out of that tradition. He also restates some of the philosophical objections to Bayesian analysis, especially questioning the use of priors.

I didn't come away from this book with any new analytic skills, though I suppose I could have mined the text more deeply. I did come away impressed by how much applied math and probability modeling have changed just since the 70s.

 
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