Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks

It explains all the concepts using very simple, funny and engaging examples! You start off with solving the probability of seeing a UFO to then analyzing a burglary at home! The examples are fantastic and the chapters are just short enough that they keep you engaged throughout. The exercises at the end are very helpful too! I've been enjoying my time learning about Bayesian Statistics

Recently I've gotten more interested into how A/B testing works. As a person who hasn't taking statistics in more than 10 years, I decided to find an approachable book that wouldn't leave me flustered. This book did just the trick. This entire book can be read within a few hours. It doesn't skimp on the mathematics but explains a lot of it via intuition. The example problems it uses are sure to keep your interest. They are original and not the least bit boring. At the end of each chapter you get to practice your skills by writing a few, albeit simple, R programs to test your knowledge. There was only one part of this book that made me go "I wish he would've explained this a bit more" which is the equation for the beta distribution. He seems to gloss over it's construction but covers how to use it, which isn't too bad since R will do the leg work for you. After reading this book I walked away with an understanding of how A/B testing software works such as launch darkly. I also have started to crack open my probability and statistics book from when I was in college and it felt much less dauting this time around. If you want to learn about parameter estimation, hypothesis testing the Bayesian way, you can't find a better intro than this one. Nostarch is constantly producing hits and this one is no exception.

I've really enjoyed the examples in this book. Well thought-out and they resonate with me. The legos piece for conditional probability was good, but a really great example of a problem is Han going through the asteroid field. A lot of us math people have seen Star Wars and we get this easily. I only wish more care was given to the problem set. I've seen the language is kind of muddy in some of the problems, and an earlier error, but I'm in chapter 16 solving problem 2 and the author not only mistakes the solution by using a wrong probability, but even in the solution writes full paragraphs of explanation that incorporate this incorrect result (i.e. not a simple math mistake). The actual answer is closer to 7.5 than 1.53, because you're using 0.83 (Schwan) and not 0.63 (earwax imp.) in calculating your P(D|H2). Anyway, great book, but if you are careful you will spot errors. Don't let that fool you though into thinking this book is a waste of time. It's a really good book.

The book was able to provide a clear and down-to-earth explanation of priors and likelihood using a single/Bernoulli parameter. Before reading this book, I had a hard time differentiating priors from likelihood/data. I always thought that the data from observations, quasi-experiments and simulation was all I had in working with the numbers; it never occurred to me that bringing in seasoned or experienced judgment through priors could formally and mathematically improve the probability of whatever I was estimating. Now, I understand why the frequentist and the Bayesian views clash. It was illuminating to have the tools to counter observations and data when you have other evidence to help in calibrating the final probability or posterior. Kudos to Will Kurt.

I purchased this book as an easy-to-read introduction to learn Bayesian statistics for scientific data analysis. The text is fluid, and the book doesn't get too wordy given its mathematical content. I would recommend this book for first time students in statistics or people stepping into data science. One of the things I dislike about mathematics/statistics textbooks is the jargon and the ambiguous symbols, which this books does an excellent job of avoiding. Be warned though, this book is about a technical subject, and there are a lot of mathematical equations, which thankfully are walked through in the text. The author uses a lot of intuitive examples to illustrate the mathematics. However, the book is rather brief, and so this book is better at accompanying, rather than replacing, statistics textbooks.

Just what my grandson wanted for Christmas.

I read this book and it walks through things in a fun and entertaining and very relatable manner. If you are new to Bayes (like I am), I would recommend writing out the definitions and keeping a journal, it makes it very easy to go back and review Beta, and some of the definitions of priors when you get to the C3PO chapter. I found myself rewriting out some of the prior chapters info as new concepts were added. This is not the kind of book you want to read without working some of the math along the way. I would say if you have a command of some of the beginning concepts in high school algebra or took stat (even better), you will be fine understanding the concepts. Artwork was beautiful, concepts were fun to read and kept my interest. Book arrived in 1 day, fast ship! Great author! Great book, highly recommend for those who are interested in the subject matter. Cannot wait for the next one.

Throughout chapters 3 through 5 there’s no mention of ‘independence’. He just says P(A,B) equals P(A)xP(B). I find myself mentally shouting “what if A and B are not independent?” He doesn’t mention that until chapter 6. He could at least say “If A and B are independent events then P(A,B) = P(A) times P(B); we’ll discuss independence in chapter 6.” In his example of taking a bus or a train, the events of the bus being late and the train being late ARE dependent, since if the bus is late lots of people might crowd into the train, and make it late. I might add some more to this review later.

I love learning about data, math, statistics and computer science. This is by far my favorite publisher and this book doesn't disappoint. Explanation and examples that are applied in a way that just makes sense. Love it.

Great start for beginners. I like how it guides you through each example at a granular level and ensures that the formulas are understood, rather than just being thrown at you to memorize.

Un livre que j'ai adoré, car il m'a réconcilié avec les Stats ! ( beaucoup d'applications dans la vie sont basées sur les Stats Bayésiennes, comme des filtres d'e-mails anti-spams, l'A/B testing dans les campagnes marketing, l'estimation de positionnement d'avions (kalman filters pour les connaisseurs) ou encore sur le fonctionnement du cerveau, le cerveau bayesien,... Puissant, non ?)... En effet, à travers des exemples amusants, on progresse sans s'en rendre vraiment compte et c'est fun, très abordable et très facile à suivre, car du niveau de compréhension d'un lycéen !!! (J'aurais aimé le trouver en français pour le faire lire par mes parents et mes sœurs ) ... J'adore sinon ces livres de maths aux éditions "No Starch Press", et je les recommanderais à quiconque aime apprendre comme moi de manière pratique en appliquant les notions en programmes informatiques, à la différence des livres de maths plus académiques et abstraits...

Un buon libro introduttivo per chi sa poco di statistica, di quella Bayesiana in particolare. Non richiede generalmente conoscenze di matematica avanzate, e comunque ha un capitolo per rinfrescare in modo qualitativo alcune nozioni di calcolo. Propone problemi ed esercizi (con le soluzioni) utili per afferrare i concetti. Richiede l'utilizzo del software statistico R e R Studio (scaricabili e utilizzabili gratuitamente). A me è stato utile, e lo consiglio.

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