In this clearly reasoned defense of Bayes's Theorem -- that probability can be used to reasonably justify scientific theories -- Colin Howson and Peter Urbach examine the way in which scientists appeal to probability arguments, and demonstrate that the classical approach to statistical inference is full of flaws. Arguing the case for the Bayesian method with little more than basic algebra, the authors show that it avoids the difficulties of the classical system. The book also refutes the major criticisms leveled against ...
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In this clearly reasoned defense of Bayes's Theorem -- that probability can be used to reasonably justify scientific theories -- Colin Howson and Peter Urbach examine the way in which scientists appeal to probability arguments, and demonstrate that the classical approach to statistical inference is full of flaws. Arguing the case for the Bayesian method with little more than basic algebra, the authors show that it avoids the difficulties of the classical system. The book also refutes the major criticisms leveled against Bayesian logic, especially that it is too subjective. This newly updated edition of this classic textbook is also suitable for college courses.
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Applications of Bayesian statistics are just now elbowing their way into service in many fields, yet statistical education for users of statistics who are not professional statisticians is still mired in "orthodoxy." This somewhat older book is an excellent introduction to the field and explains the arguments and counter-arguments clearly and in a studied, logical fashion. There is a certain amount of polemic (par for the course), but polite in a British kind of way (nothing like the rough-and-tumble anti-frequentist zingers of ET Jaynes). The math is accessible to anyone who took algebra, and is present in just the right quantity.
