A Treatise on Probability The Connection Between Philosophy and the History of Science John Maynard Keynes 9781434406965 Books
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John Maynard Keynes, 1st Baron Keynes, CB (1883-1946) was a British economist whose ideas have profoundly affected the theory and practice of modern macroeconomics, as well as the economic policies of governments. He greatly refined earlier work on the causes of business cycles, and advocated the use of fiscal and monetary measures to mitigate the adverse effects of economic recessions and depressions. His ideas are the basis for the school of thought known as Keynesian economics, and its various offshoots. "A Treatise on Probability," originally published in 1921, launched the "logical-relationist" theory.
A Treatise on Probability The Connection Between Philosophy and the History of Science John Maynard Keynes 9781434406965 Books
Were my understanding of statistics on a higher level I may have given this book five stars. I found it difficult to keep up with Keynes's analysis which seemed at times to go beyond graduate level material. In certain instances, I realized that the gap in my understanding was due to a lack of common modern day statistical terms and references. It took me half the book to realize that the inverse probability theorem was Bayes formula. Even in describing confidence intervals in part 5 the terms were difficult to avoid re-reading for comprehension. If you do not have at least a graduate-level understanding of mathematical statistics and probability, I don't think this should be the next book you read. If you do have that training, be ready to translate terms used in the first half of the twentieth century (and much older when he is referencing previous works) to modern day terms to reach full comprehension.Moving on, this book is a great introduction to the many different names and theories in statistical inference and induction (theory of indifference, frequency theory, Laplace's rule of succession...etc.). Despite its extremely technical content, Keynes will give analogies and examples to express his ideas often. Even for those who are novices in stastical calculation, parts 1, 4, and 5 will provide great insight into how intelligent decision making should be approached and when to be skeptical of probability claims. Keynes covered many biases in data analysis, as well as the problem of induction in his discussion on negative and positive analogies in inductive observation. His discussion at the end about non-statistical or unstable probabilities is essentially an attack on the assumption of normality. It no longer surprises me when I read Keynes's work and find reference to ideas similar to today's common knowledge. Right or wrong, surely thought-provoking.
All in all, for his brilliance in part 1 alone, this book was worth the read.
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A Treatise on Probability The Connection Between Philosophy and the History of Science John Maynard Keynes 9781434406965 Books Reviews
The content is what I was curious about. The printing is too light. And yet the price is good. I have just start to read it and is really good writing though.
my husband is reading the book.
There are various reprints available of A Treatise on Probability by Keynes and many reviews have been written. Some of the reviews complaint that they bought exemplars that were of low printing quality. I ordered a version from Wildside Press (September 16, 2010)
and this version is of excellent quality. You can use the feature 'look inside' to convince yourself.
I thought given the fact that there seems to be a problem with the printing quality of the book from other publishers, this information might be of interest for other people considering to buy this classic.
The Treatise on Probability provides unique difficulties for a contemporary reviewer. On the one hand, Keynes is almost universally wrong in critiquing early twentieth century statistical theory from a scientific perspective. The law of large numbers, maximum likelihood estimation and least squares regression have become the backbone of modern statistics despite his criticism. And in the philosophical realm, Popperian falsification has won out over Keynesian inductivism. On the other hand, some of Keynes' ideas seem to be precursors of modern statistical theory. For example, the idea of negative analogy anticipates Fisher's notion of randomization in experimental design. Though I am no expert in the philosophy of probability, I think the Keynesian theory that statements have a probability associated with them as opposed to a fundamentally stochastic universe has its modern day adherents as well.
Despite these errors in judgment, A Theory of Probability is nonetheless a brilliant work synthesizing and advancing what was then the relatively new science of statistics. It is probably one of the last of its kind in that it could produce a bibliography of only thirty-five pages that encompassed not a survey but the entire body of probability theory.
Should a contemporary scientist read this? Maybe there are ideas about inductive reasoning that can be leveraged in modern statistics, though one would have to have a pretty masterful grasp of theory to separate the wheat from the chaff. Possibly some philosophers of science could read this for arguments that question the Popperian perspective.
Even so, while obviously the work of a genius who has a real handle on early twentieth century philosophy and statistics, my opinion is that most researchers should simply pass on this book. Philosophy and science have rejected Keynsian arguments and unless one has a penchant for dated scientific theories there isn't a lot to gain from reading a five hundred page treatise of theories now mostly passé.
The edition, meaning type-setting, pagination and font are all good and Keynes is a lively writer so if this is the kind of reading you enjoy by no means let me dissuade you. But if you are interested in learning the antecedents of modern science or philosophy you will be disappointed. Difficult to say of a great man, both as an economist and writer, but, with the benifit of hindsight, this is a much lesser researcher's modest opinion.
Were my understanding of statistics on a higher level I may have given this book five stars. I found it difficult to keep up with Keynes's analysis which seemed at times to go beyond graduate level material. In certain instances, I realized that the gap in my understanding was due to a lack of common modern day statistical terms and references. It took me half the book to realize that the inverse probability theorem was Bayes formula. Even in describing confidence intervals in part 5 the terms were difficult to avoid re-reading for comprehension. If you do not have at least a graduate-level understanding of mathematical statistics and probability, I don't think this should be the next book you read. If you do have that training, be ready to translate terms used in the first half of the twentieth century (and much older when he is referencing previous works) to modern day terms to reach full comprehension.
Moving on, this book is a great introduction to the many different names and theories in statistical inference and induction (theory of indifference, frequency theory, Laplace's rule of succession...etc.). Despite its extremely technical content, Keynes will give analogies and examples to express his ideas often. Even for those who are novices in stastical calculation, parts 1, 4, and 5 will provide great insight into how intelligent decision making should be approached and when to be skeptical of probability claims. Keynes covered many biases in data analysis, as well as the problem of induction in his discussion on negative and positive analogies in inductive observation. His discussion at the end about non-statistical or unstable probabilities is essentially an attack on the assumption of normality. It no longer surprises me when I read Keynes's work and find reference to ideas similar to today's common knowledge. Right or wrong, surely thought-provoking.
All in all, for his brilliance in part 1 alone, this book was worth the read.
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