Scientific Reasoning: The Bayesian Approach | ||

Howson (Colin) & Urbach (Peter) | ||

This Page provides (where held) the Abstract of the above Book and those of all the Papers contained in it. | ||

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**BOOK ABSTRACT: **__Back Cover Blurb__

- Scientific reasoning is — and ought to be — conducted in accordance with the axioms of probability. This Bayesian view — so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth century — has, in the last twenty years, experienced a huge upsurge of interest among statisticians, scientists, and philosophers, and is fast becoming the new methodological paradigm.
*Scientific Reasoning: The Bayesian Approach*explains, in an accessible style, those elements of the probability calculus that are relevant to Bayesian methods, and argues that the probability calculus is best regarded as a species of logic.- Howson and Urbach contrast the Bayesian with the 'classical' view that was so influential in the last century, and demonstrate that familiar classical procedures for evaluating statistical hypotheses, such as significance tests, point estimation, confidence intervals, and other techniques, provide an utterly false basis for scientific inference. They also expose the well-known non-probabilistic philosophies of Popper, Lakatos, and Kuhn as similarly unscientific.
- Scientific Reasoning shows how Bayesian theory, by contrast with these increasingly dis-credited approaches, provides a unified and highly satisfactory account of scientific method, an account which practicing scientists and all those interested in the sciences ought to master.
- "
*Scientific Reasoning*is an energetic and provocative defense of Bayesianism, full of interesting arguments, puzzles, and examples. It is sure to interest philosophers, statisticians, and scientists who use statistical methods."

→ Elliott Sober - "The second edition was already the most comprehensive, up-to-date, and authoritative book on the foundations and applications of Bayesian methods. This new third edition is even more comprehensive and up-to-date — and equally authoritative — which is no mean feat! This is a must-read and must-own for anyone — Bayesian or otherwise — with interests in the philosophy of science or statistics."

→ Branden Fitelson, University of California, Berkeley **Colin Howson**is the author of*Logic with Trees*(1997) and*Hume's Problem*(2003).**Peter Urbach**is the author of*Francis Bacon's Philosophy of Science*(1987) and editor-translator (with John Gibson) of Bacon's*Novum Organum*(1994).

Open Court, 2006. Paperback.

"

- How should hypotheses be evaluated, what is the role of evidence in that process, what are the most informative experiments to per-form? Questions such as these are ancient ones. They have been answered in various ways, often exciting lively controversy, not surprisingly in view of the important practical implications that different answers carry. Our approach to these questions, which we set out in this book, is the Bayesian one, based on the idea that valid inductive reasoning is reasoning according to the formal principles of probability.
- The Bayesian theory derives from the Memoir of the mathematician and divine, Thomas Bayes, which was published posthumously by his friend Richard Price in 1763. The principles set out by Bayes had considerable influence in scientific and philosophical circles, though worries about the status of the prior probabilities of scientific theories meant that the whole approach continued to be dogged by debate. And by the 1920s, an alternative approach, often called ‘Classical’, achieved dominance, due to powerful advocacy by R. A. Fisher and many other distinguished statisticians, and by Karl Popper and similarly distinguished philosophers. Most of the twentieth century was dominated by the classical approach, and in that period Bayesianism was scarcely taught in universities, except to disparage it, and Bayesians were widely dismissed as thoroughly misguided.
- But in recent years, there has been a sea-change, a paradigm shift. A search of the Web of Science database shows, during the 1980s. a regular trickle of around 200 articles published annually with the word or prefix ’Bayes’ in their titles. Suddenly, in 1991, this number shot up to 600 and by 1994 exceeded 800; by 2000 it had reached almost 1,400. (Williamson and Corfield. 2001, p. 3). This book was one of the first to present a comprehensive, philosophical case for the Bayesian approach to scientific reasoning and to show its superiority over the classical. Its first and second editions were published in 1989 and 1993, and from the figures quoted it is clear that the book anticipated a powerful and sophisticated resurgence of the once-dominant Bayesian approach.
- This new edition amends, updates, re-organizes, and seeks to make the subject more accessible. The text is intended to be self- contained. calling, in the main, on only elementary mathematical and statistical ideas. Nevertheless, some parts are more complex, and some more essential to the overall argument than others. Accordingly, we would suggest that readers who are not already familiar with mathematical probability but who wish to gain an initial understanding of the Bayesian approach, and to appreciate its power, adopt the following plan of attack.
- First, read Chapter 1, which sets the scene, as it were, with a brief historical overview of various approaches to scientific inference.
- Then, look at section a of Chapter 2, which gives the simple principles or axioms of the probability calculus, and section b, where there are some of the probability theorems that will be found useful in the scientific context: the central theorem here is Bayes’s theorem in its various forms.
- We then suggest that the reader look at the first few sections of Chapter 4, where Bayes’s theorem is applied to some simple reasoning patterns that arc found particularly when deterministic theorems are handled; this chapter also compares the Bayesian approach with some others, such as Popper’s well-known falsificationist methodology.
- Chapters 5 to 7 deal with non-deterministic, that is, statistical hypotheses, giving a critical exposition of the classical, or frequentist, methods that constitute the leading alternative to the Bayesian approach: the main classical ideas can be gleaned from sections a to d and f and g of Chapter 5.
- The final part of the mini-course we are suggesting is to examine Chapter 9, where some of the more widespread criticisms that have been levelled against the Bayesian approach are discussed (and rejected).

- There are some marked differences between this third edition and the preceding ones. For example, some of the objections to the Bayesian theory we considered in the second edition have not stood the test of time. There have also been changes of mind: one of the most prominent examples is the fact that now we accept the strength of de Finetti’s well-known arguments against countable additivity, and have accordingly dropped it as a generally valid principle. Other changes have been largely dictated by the desire to make this edition more compact and thereby more accessible. We hope that this indeed turns out to be the case.

"

- The Problem of Induction – 1
- Popper on the Problem of Induction – 2
- Scientific Method in Practice – 3
- Probabilistic Induction: The Bavesian Approach – 6
- The Objectivity Ideal – 9
- The Plan of This Book – 10

"

- The Axioms – 13
- Useful Theorems of the Calculus – 16
- Discussion – 22
- Countable Additivity – 26
- Random Variables – 29
- Distributions – 30
- Probability Densities – 31
- Expected Values – 32
- The Mean and Standard Deviation – 33
- Probabilistic Independence – 35
- Conditional Distributions – 37
- The Bivariate Normal – 38
- The Binomial Distribution – 39
- The Weak Law of Large Numbers – 41

"

- Prologue: Frequency-Probability – 45
- Measuring Uncertainty – 51
- Utilities and Probabilities – 57
- Consistency – 63
- The Axioms – 67
- The Principal Principle – 76
- Bayesian Probability and Inductive Scepticism – 79
- Updating Rules – 80
- The Cox-Good Argument – 85
- Exchangeability – 88

"

- Bayesian Confirmation – 91
- Checking a Consequence – 93
- The Probability of the Evidence – 97
- The Ravens Paradox – 99
- The Duhem Problem – 103
- Good data, Bad Data, and Data Too Good to be True – 114
- Ad Hoc Hypotheses – 118
- Designing Experiments – 127
- Under-Determination and Prior Probabilities – 128
- Conclusion – 130

"

- Falsificationism in Statistics – 131
- Fisherian Significance Tests – 133
- Neyman-Pearson Significance Tests – 143
- Significance and Inductive Significance – 149
- Testing Composite Hypotheses – 161
- Classical Estimation Theory – 163

→ 1. Point Estimation – 163

→ 2. Interval Estimation – 169 - Sampling – 177
- Conclusion – 181

"

- Clinical Trials: The Central Problem – 183
- Control and Randomization – 185
- Significance-Test Defences of Randomization – 188
- The Eliminative-Induction Defence of Randomization – 194
- Sequential Clinical Trials – 197
- Practical and Ethical Considerations – 202
- Conclusion – 203

"

- Simple Linear Regression – 205
- The Method of Least Squares – 207
- Why Least Squares? – 209
- Prediction – 217
- Examining the Form of a Regression – 220
- Conclusion – 235

"

- The Question of Subjectivity – 237
- The Principle of Stable Estimation – 245
- Describing the Evidence – 247
- Sampling – 252
- Testing Causal Hypotheses – 254
- Conclusion – 262

"

- The Charge of Subjectivism – 265

→ The Principle of Indifference – 266

→ Invariance Considerations – 273

→ Informationlessness – 276

→ Simplicity – 288 - Summary – 296
- The Old-Evidence Problem – 297
- Conclusion – 301

- Blue: Text by me; © Theo Todman, 2020
- Mauve: Text by correspondent(s) or other author(s); © the author(s)

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