Enjoy fast, FREE delivery, exclusive deals and award-winning movies & TV shows with Prime
Try Prime
and start saving today with Fast, FREE Delivery
Amazon Prime includes:
Fast, FREE Delivery is available to Prime members. To join, select "Try Amazon Prime and start saving today with Fast, FREE Delivery" below the Add to Cart button.
Amazon Prime members enjoy:- Cardmembers earn 5% Back at Amazon.com with a Prime Credit Card.
- Unlimited Free Two-Day Delivery
- Instant streaming of thousands of movies and TV episodes with Prime Video
- A Kindle book to borrow for free each month - with no due dates
- Listen to over 2 million songs and hundreds of playlists
- Unlimited photo storage with anywhere access
Important: Your credit card will NOT be charged when you start your free trial or if you cancel during the trial period. If you're happy with Amazon Prime, do nothing. At the end of the free trial, your membership will automatically upgrade to a monthly membership.
Buy new:
$89.99$89.99
FREE delivery:
Friday, June 23
Ships from: Amazon.com Sold by: Amazon.com
Buy used: $80.04
Other Sellers on Amazon
& FREE Shipping
92% positive over last 12 months
& FREE Shipping
89% positive over last 12 months
+ $3.99 shipping
84% positive over last 12 months
Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required. Learn more
Read instantly on your browser with Kindle for Web.
Using your mobile phone camera - scan the code below and download the Kindle app.
Probability Theory: The Logic of Science Annotated Edition
-
90 days FREE Amazon Music. Terms apply.
90 days FREE of Amazon Music Unlimited. Offer included with purchase. Only for new subscribers who have not received offer in last 90 days. Renews automatically. You will receive an email to redeem. Terms apply. Offered by Amazon.com. Here's how (restrictions apply)
Purchase options and add-ons
- ISBN-100521592712
- ISBN-13978-0521592710
- EditionAnnotated
- PublisherCambridge University Press
- Publication dateJune 9, 2003
- LanguageEnglish
- Dimensions7.25 x 1.5 x 10.25 inches
- Print length753 pages
Frequently bought together

What do customers buy after viewing this item?
- Lowest Pricein this set of products
Probability Theory: A Concise Course (Dover Books on Mathematics)Y.A. RozanovPaperback - Most purchased | Highest ratedin this set of products
Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)Hardcover
Special offers and product promotions
- 90 days FREE of Amazon Music Unlimited. Offer included with purchase. Only for new subscribers who have not received offer in last 90 days. Renews automatically. You will receive an email to redeem. Terms apply. Offered by Amazon.com. Here's how (restrictions apply)
Editorial Reviews
Review
Science
"This is a work written by a scientist for scientists. As such it is to be welcomed. The reader will certainly find things with which he disagrees, but he will also find much that will cause him to think deeply not only on his usual practice by also on statistics and probability in general. Probability Theory: The Logic of Science is, for both statisticians and scientists, more than just 'recommended reading': It should be prescribed."
Mathematical Reviews
"The rewards of reading Probability Theory can be immense."
Physics Today, Ralph Baierlein
This is not an ordinary text. It is an unabashed, hard sell of the Bayesian approach to statistics. It is wonderfully down to earth, with hundreds of telling examples. Everyone who is interested in the problems or applications of statistics should have a serious look.
SIAM News
"The author thinks for himself and writes in a lively way about all sorts of things. It is worth dipping into it if only for vivid expressions of opinion; There are many books on Bayesian statistics, but few with this much color."
Notices of the AMS
Book Description
Product details
- Publisher : Cambridge University Press; Annotated edition (June 9, 2003)
- Language : English
- Hardcover : 753 pages
- ISBN-10 : 0521592712
- ISBN-13 : 978-0521592710
- Item Weight : 3.62 pounds
- Dimensions : 7.25 x 1.5 x 10.25 inches
- Best Sellers Rank: #125,833 in Books (See Top 100 in Books)
- #16 in Statistics (Books)
- #24 in Mathematical Physics (Books)
- #121 in Probability & Statistics (Books)
- Customer Reviews:
About the author

Discover more of the author’s books, see similar authors, read author blogs and more
Customer reviews
Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.
To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.
Learn more how customers reviews work on Amazon-
Top reviews
Top reviews from the United States
There was a problem filtering reviews right now. Please try again later.
The ideas and messages of this book significantly differ from what is taught in pretty much all other statistics books. Here is one example, the Gaussian distribution is heavily used in statistical analysis. Most textbooks are pretty much apologetic about this overuse of the Gaussian distribution and struggle to suggest alternative methods. Jaynes, on the other hand, says (in Chapter 7) that the range of validity for the application of the Gaussian distribution in data analysis is actually "far wider that is usually supposed".
A major highlight of the book is the focus on history. Very careful historical accounts are presented as to how the greats of the field (like Gauss, Laplace, Cox, Fisher etc) approached data analysis. This stuff again cannot be found in any other book in the field. I have been using this book heavily in pretty much anything I teach these days and, as a consequence, teaching statistics has been a much more pleasurable experience than before.
Jaynes apparently originally wanted to write a sequel to this book focussing on more advanced applications. It is a pity that he passed away before he could write the sequel.
I recommend readers to the outstanding books by MacKay and by von der Linden-Dose-von Toussaint for numerous interesting and nontrivial applications of Probability Theory (Bayesian Statistics) to Data Problems.
I would also like to recommend (as sequels to reading Jaynes) the books of David Blower which clarify and complement the ideas of Jaynes. For readers interested in learning more about the various issues, pitfalls and shortcomings of Frequentist "Orthodox" statistics, I would like to recommend the collected works of Dev Basu.
Summarizing the content: The book very exhaustively demonstrates how Bayesian statistical approaches subsume rather than compete with "orthodox" (sampling theory-derived) statistics. Importantly, it begins by deriving the sum and product rules (which in other texts are typically presented as axioms) from "common sense" considerations. In other words, what is usually treated as "given" in other statistics texts is shown to, in fact, depend on even more fundamental (and, thus, indisputable) considerations of what constitutes rational plausible reasoning. This places the whole endeavor of statistics on firmer ground than any other text I've seen. The book is worth buying for the first few chapters alone, but it just gets better from there.
Jaynes goes on to link Bayes rule to information-theoretic considerations and build up probability as an extended form of logic (as the title implies). In some cases this yields a new and deeper understanding of "orthodox statistical practice." In others it exposes (and explains) the absurdities of strictly frequentist approaches. Again, I have rarely learned so much from one book.
One caveat: It does not at all require a statistics background, but, obviously, some of Jaynes (mildly polemical) discourse will, of course, be lost on you without it.
It is true Jaynes' style is caustic against positions that are contrary to his owns. But he is very convincing on the reasons he gives to pinpoint the big holes in the so called "orthodox" school of probability and statistics.
Besides, the book is very lengthy, without being prolix, on its explanations, making it very pedagogical. Constrasting with that, nevertheless, Jaynes sometimes proposes examples that I believe only a mathematician or physicist with specific knowledge of the subject mentioned by the author will be able to follow. But those parts do not impact understanding of the main ideas.
It must be noted also that "Probaility theory: the logic of science" is mainly a theory book. Its goal is to present probability as an extension of deductive logic. It only brings a small number of exercises.
The best thing about this book, at least for me, is having a style that really makes me look forward reading the next page, something very rare for a technical book. In fact, the only other book I came across that had that virtue was the "Feynman Lectures on Physics".
Top reviews from other countries
The book consists of two parts (each about 300 pages long): Principles and elementary applications (1) and Advanced applications (2).
The bulk of part one is about Bayes' rule (or rule of inverse probability). Instead of calculating the probabilities of outcomes of some random experiments with known parameters, the probability distribution of the parameters, now supposed unknown, can be calculated from recorded outcomes of the random experiment. Actually Bayes rule is not restricted to random experiments, but has a much wider scope. This is discussed at length, in a general way and with examples.
The second part of the book starts with the principle of maximum entropy as a means of generating prior probabilities. The principle is powerful when the probabilities of the random experiment are subject to restrictions. Other topics are decision theory, physical measurements, model comparison and communication theory.
I find the topics of the book mostly very interesting, and as far as I can judge the author has made some important contributions. On the other hand the book has many severe flaws.
1) Jaynes simply makes too many words about everything he explains. He digresses all the time. He repeats himself often (and admits it ! p. 336: "We belabor still another time, what we have already stressed many times before."). It is difficult to find the usefull concepts and formulas in this torrent of words.
2) Given the size of the book, there are too few numerical examples in it.
3) I also find that the mathematical presentation is not first class.
4) I dislike the way the author presents his own ideas as giving the best insight. Sometimes he just does not understand the ideas of other authors.
5) Jaynes couldn't completely finish his book in his lifetime, and this shows in several chapters. His working colleague and editor G. L. Bretthorst didn't want to make the necessary cuts after his death. K. S. van Horn has put a long Errata of Jaynes book on internet. There are plenty of misprints and errors in the book.
6) Even in some of the finished chapters the topics are badly expounded. As an example I mention the last chapter, Introduction to Communication theory. It is a difficult read. The same content (and more) was first published by C. G. Shannon in the treatise "A mathematical theory of communication theory", available online. Shannon's treatise is excellent. Why write a new introduction to Communication theory when the original is so good?
7) To understand one of the mayor flaws of the book, you must know that there were two schools of probability theory in the 20th century. The Bayesian school continued the work of Laplace, and made extensive use of Bayes' rule. The orthodox school denied the general validity of some of Laplace's views and of Bayes' rule, and invented statistics as a separate scientific domain. The two schools were at war with each other, and the scientific journals were their battle field. A considerable part of the book is dedicated to comparing the results of the two schools in different classes of problems. Of cause Jaynes, who belongs to the Bayesian school, always comes to the conclusion that the Bayesian approach gives better or quicker results than the orthodox approach. As to general theorems, the Bayesian theorems are based on axioms, the orthodox theorems are "ad hockeries". Jaynes also dedicates a chapter to making a psychogram of some of the high priests of each school. Of course the Bayesian high priests are likeable, and the orthodox high priests are detestable. This is very bad: you cannot be a fair advocate of a theory you don't like. Jaynes should have explained the Bayesian viewpoint alone. Mentioning that there is the orthodox school with different views on probability theory would have been sufficient.
Conclusion: I advise you against buying this book. There must be better books on Bayesian Probability Theory available.
His description of probability distributions as "carriers of uncertain information about unknowns" rather than the traditional and flawed classical view of "behaviour of selected summary statistics in the limit of an infinite amount of repeated random events" (whatever that means!) is an indicator of the different perspectives.
Anyone who wants to understand what probability theory actually _is_ at a fundamental level and have their mind opened up to how they can apply it in their area should have a look and strap themselves in for the ride. Highly recommended.
If you want a more compact and introductory book with an applied focus and examples then I strongly recommend Sivia & Skilling:
Data Analysis: A Bayesian Tutorial
La mayoría de casos que trata son bastante sencillos, muy tratables analíticamente. Quizás debe ser complementado con algun tratado más aplicado, pero como base de la probabilidad Bayesiana, me parece fantástico.











