A Visual Introduction For Beginners - Bayes' Theorem Examples

Overall it fulfills the objective in the title, but there were some things that I think could be easily improved upon:

1. I found the overall organization of the book confusing, even though it is a short book. It's fine if you just read it beginning to end as I did, but if you are trying to figure out "Intro" vs. "Scenarios" in "Example Section 1" vs. "Example Section 2", etc., particularly with the amount of repetition within each section and between sections. Even the "Booklet Structure" page didn't really make it clear for me.
2. In the first "Visual Intro" example (before "Scenario 1") with drawing cookies out of a box (and in the YouTube video that has this same example), the explanation for why P(CC Cookie) is .75 is because there are 20 cookies total in both boxes, 15 of which are chocolate chip, and 15/20 is .75. But in my mind that is not an accurate representation of how you find P(CC Cookie) (even though in this case it does come up with the right result of .75). If box A still had 10 chocolate chip cookies and box B had only TWO cookies--one of which was chocolate chip and one of which was peanut butter--the probabilities would all work out the same, including the fact that P(CC Cookie) would be .75, even though clearly 11/12 is not .75. A decision tree approach would have come up with the right answer FOR THE RIGHT REASON, but decision trees aren't explained until later in the book.
2. When the scenarios (flu, breathalyzer, surprise attack) in a later section of the book, some of the probabilities in the problem (and therefore the result) are changed! What is the point of repeating an example if you are going to change the probabilities used? Ex. P(B|A) is .9 originally but .75 in the second flu example. It looks like it was done intentionally, since the given probabilities were different for all three scenarios the second time around.
3. I liked the fact that they used Bayes to calculate the probability of two different outcomes so those could be compared to each other (flu vs. food poisoning), but I really would have like to have seen an example of using the formula twice because of NEW INFORMATION, such as a second, independent test being done. Part of the book treated the P(A) as the "new information", but I think it is more obvious one of the big benefits of using Bayes is if you first calculate a probability P(A | B), but then use this as the new P(A) after you have already gotten result B and calculate a P(A | B1 and B2) where you just use the result of the first calculation as your new P(A) and use the formula as usual, only this time with test outcome B2 (a different, independent test) involved.

I did like the fact that one scenario was intentionally included where the posterior probability doesn't differ wildly from the conditional probability P(B | A) to show that Bayes doesn't always produce nonintuitive results.

A very brief explanation of theory.
Two main methods to show the mechanics: Venn and formula
Very well structured scenario style cases, provoking to think and emulate yourself in that situation.
Surprising outcomes challanging your daily thinking and perception of reality.
I wish it had an interactive web site accompanying the book calling for business cases from readers.

An easy read, and well explained. Author should correct the decision tree for the "Breathalyzer" in Scnario 2.1. The probabilities for "DY",NDY", and "NDN" were apparently copied from an earlier example and don't compute properly. (ie - .003 x .98 does not yield .0375). Perhaps this is just in the Kindle version? No complaints otherwise: well worth the money!

I first learned about the Bayes Theorem in graduate school in the late 1960's, but I've always been too math-challenged to make sense of it. This book is the most useful explanation I have come across, and I appreciate the high redundancy (something I really need.)

I have high hopes, because Bayes' Theorem has always been difficult for
me to get my head around. I understand the theory, but understanding
of the practical implications must follow ...

Have to admit, I haven't read this yet. Saving it for a near-future long
airline flight. :-)

The Bayes theorem is a formal version of what the intelligent person does intuitively. But the formalization is not intuitive. Relatively complete as an introduction. If this doesn't make Bayesian probability clear, you should stick to arithmetic.

Dan applies Bayes to simple but practical problems using three different approaches: venn diagrams, decision trees and, of course, the Bayes equation itself. Such approach builds our intuition gradually using repetition to sediment the understanding. He always point out where our "non-Bayesian intuition" fails and shares references for further studies.

I am a very logical, systematic thinker, and this book hit the sweet spot for me! The material was well-presented and each problem thoroughly solved. For an introductory guide, I couldn't have asked for anything more.

Thank you!

Updated Review: Contacted publisher and they sent a gift card to partly cover the kindle version's cost. However, it was in CDN not USD, so I couldn't use it, but I appreciated the effort anyway. They also said they are aware of the issue, and plan to fix this in the new year. I wish they wouldn't sell the print version until it was fixed, but at least they aim to fix it soon.

Original Review: Received the print version of this book as a gift. The content is not adapted for the print version. The content relies heavily on the reader's ability to click on linked text. Urls are not listed in print book to compensate. There is no reference in the back or a website listing the urls for you. Really disappointing. I was excited when I first saw it. Contacted the store - asked about free access to kindle version since this book is in their matchbook program and the book does not provide urls. They responded full refund if returned accepted only, no access to kindle version granted. Bummer.

The book is simple and direct. True to its title, it is an introduction to Bayes' theorem and lacks a deep understanding of the subject. But the examples and applications that it provides are fascinating, and have induced me to search for other books that can provide me with a deeper understanding of the subject. As as introductiion, I recommend it highly!

This book takes what can be a daunting and complex subject and breaks it down with a series of easy to follow examples which buildup to deliver a great overall explanation of how to use Bayes Theorem for basic analysis and even off-the-cuff critical thinking. The book parallels some video content which delivers the same examples through a lecture given to students. I found them very helpful to watch as I read through the book.

"The numbers are your friends" is a brief and helpful phrase I learned from my statistics teacher early in my career. I also learned that a great teacher has the ability to simplify and clarify ideas so they may be quickly grasped. That's what Dan Morris does for us in this introductory book on probability theory. The "visual" part makes what could be confusing concrete and clear. Thanks, Dan, for the hard work in a very useful book. A great teacher makes all the difference!

I picked this up because of the price and thought that is might be interesting - and it was! My only problem? I skipped the beginning and missed the chapter on changing my reading settings. If you are considering purchasing it, I highly recommend following the instructions on the "Don't Waste Your Time" page ( especially if you are reading on a Kindle E-ink)! Doing this will help the images be in the right place, and make your reading experience much better. Overall though, a great intro to a very important topic.

The explanations on the book are very clear and the use of Venn Diagrams and decision trees
truly gives meaning to the formula.

It also use the same 3 examples again and again with small variations which engage the brain and builds skill through repetition.
If you are new to the field of Bayesian Inference and probabily in general
Read this book first to build intuition and then go to the bigger tomes.

“Repetition is the mother of learning, the father of action, which makes it the architect of accomplishment.” (Zig Ziglar)

Anyone interested in probability, odds, gambling, knowledge, or just making good decisions will appreciate this book. It oversimplifies a lot, but does so for the readers' benefit. The author uses interesting scenarios and repetitive language to help you retain each lesson. If you decide to go further, Mr Morris provides more in depth resources to pursue. I would recommended this book to anybody and everybody.

The title of the review states most of my opinion. It's an okay read via Kindle Unlimited, but I would never purchase it. Watch out for the overreaching interpretations the author makes of stats such as 5% will get flu at some point in a year is the same as 5% having the flu right now. Positive breathalyzer tests are more than 7% likely to be true, and you don't need to flee immediately to the doctor every time you get a headache because food poisoning was the ailment you bothered to investigate leading to a 60% likelihood you have food poisoning incurable without antibiotics. Read Mate Silver's book instead.

Truly excellent resource for those just getting started with Bayes -- especially for those who are visual learners. I haven't found a more clearly expressed and useful introduction out of the many "introductions" I've looked at.

This book is a simple presentation of the Bayes theorem, with several examples whom can be useful in the pratic activity. The Bayes theorem is important in Statistics because it allows to induce new events from a past knowledge. Clearly the new information doesn'be true "a priori", but only in a probability sense.