An Introduction to Probability and Inductive Logic

This is a gem of a book to get a better intuition about probability, different kinds of probabilities, and the relation to logic. I loved Ian's exposition style: clear and elaborate; no "left to reader" remarks. It has just the necessary and simple math to get the fundamentals and ideas. Also, the historical bits about the pioneers in the field of probability at the end of each chapter was interesting.

I love this book. It is easy to read and provides excellent examples. Not only does it introduce the reader to Bayes' Theorem, but it also covers various gambler's fallacies. A nice addition is the lucid philosophical commentary that keeps the reader informed about the various debates about inductive logic that have taken place over the ages. With this book, a beginner can get up-to-date with the theorem (Bayes') that has recently taken both the philosophy of science and probability worlds by storm.It is great to see a solid logic book for philosophy that is not deductive. Inductive logic is important too!

An Introduction to Probability and Inductive Logic is a very good book. The explanations are clear, the examples are lively and the book does a good job of surveying the major points of this very important topic.I wish that other reviewers had stated more clearly that the intended audience is undergraduates. While the book is worth reading for all the reasons given above, as well as others mentioned by other reviewers, those of us with some prior knowledge of mathematical probability theory and its philosophic interpretation will find most of the book contains already well understood material. For example, while I found the responses to Hume's objections to inductive knowledge very interesting, I did not need a review of the normal distribution, confidence intervals or the Law of Large Numbers. If I were to teach these subjects to bright undergraduates who were not science majors, however, I might use this material.In short, a wonderful book if you keep in mind the intended reader.

(FOUR AND A HALF STARS)This is more an intro to the PHILOSOPHY of probability and inductive logic than an intro to the MATHEMATICS of probability of inductive logic, although some of the basic mathematical ideas are covered (which is useful if you're gonna discuss the philosophy). Do not get this book if you're just looking for a typical mathematical intro to statistics. But DO get this book if you want to know about the foundations of Bayesianism or are interested in the Frequentists vs. Bayesian debate. It is the best intro out there on the Frequentists/Bayesians issue, and it is extremely helpful for someone who is trying to get a handle on Bayesian reasoning. Also, those who are more into the mathematical aspects of probability could find this book useful in giving them a wider perspective on the subject. On the whole, it's clearly written and fun to read, although it is not an "easy" book. A basic knowledge of probability theory and some initial grasp of induction are good to have before reading this. But overall, it's highly recommended for those who want to know about the conceptual underpinnings of probability/induction in general, and Bayesian and Frequentism specifically.

I use the product to teach a course in college.

Complete waste of money.

This closely reflects my own (college law) teaching style: use the smallest words and simplest examples possible, to start with, and then add on the technical terminology. Starting from a very ad hoc knowledge of logic and probability, and wishing to become formally proficient, I have been digging through various books on these topics to find one remedial enough to suit me (and the more advanced books in my library are thus waiting for later perusal). Bingo! Some highbrows may feel this style is "spoon-fed" or "dumbed down." However, a good teacher, to my mind, is one who builds a good steady bridge from where the student actually is, to more advanced concepts, and this serves admirably.

I would HIGHLY recommend this book for anyone (including business men) who must make decisions with incomplete information and under uncertainty. Instead of focusing on the mechanics of statistics, it focuses on how to think about risky propositions.I bought this book while working on a particular problem in machine learning, at a point where I had started realizing that I was losing clarity on my definition of probability. I was using the mechanics, but didn't clearly understand why the use was valid. This seemed an odd and embarrassing circumstance at the time, how could I not understand what "probability" means? As it turns out this confusion is one shared broadly in history of science, and in current applications of statistical mechanics.Prof Hacking's writing is clear and entertaining, clearly aimed at engaging the reading audience.