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

By far one of the most entertaining books I have ever read on the topic. You rarely hear "entertaining" and "statistics" in the same sentence. I originally decided to read the first few pages to see what this was whole about, only to realize that few hours have passed, and i was in a complete state of flow. This book grew my interest in more advanced topics, and I still refer to it from time to time when I need a simple explanation.The effort the author has put to describe the ideas in an elegant way is commendable. Outstanding book.

I purchased this book because some projects we've begun at work will require the use of Bayesian statistics. My hope was that this book would help understand this topic. I was extremely pleased to find out that not only does Mr. Kurt do a phenomenal job of explaining Bayesian statistics, but he also outlines other complex statistical concepts in an easy-to-grasp manner. The cherry on top is that it's actually fun to read. I've found myself reading this book for pleasure, which I can't say about many mathematical texts. Kudos to you, Mr. Kurt, and keep up the good work!

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.

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 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.

The focus of my professional work is the development and analysis of trading systems. My constant challenge is understanding, monitoring, and measuring the relationships between the model, the data it processes, the target it predicts, and the results it produces.Managing trading systems requires comparing real-time results with benchmarks, estimating risks, and adjusting position size. Being able to estimate confidence intervals is critically important. Bayesian analysis is the primary technique I use.Mr. Kurt clearly explains how individual data values form distributions, and how the shape and metrics of the distributions change as new data is observed. He outlines the theory underlying Bayesian analysis, carefully identifying each of the components and explaining how each is determined. He describes calculation of credible intervals of confidence for means, and probabilities that the means of distributions are different. The descriptions of techniques are clear and presented with a minimal amount of math.This book is an excellent introduction to the techniques used in modeling and managing trading systems.

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.

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.

If you are someone who always thought of getting the intuition behind probability and statistics, this is the THE book. It will make you fall in love with the subject explain each topics so clearly that you would be able to explain it to any high school student to a graduate graduate student with similar ease

I bought a paper book after reading a digital version.The book was in poor state with skid mark on the side, pink stain inside the cover and cover bent. It doesnt feel like a new book :(

I think Will Kurt has done a great job here.It's not a big book but covers quite a lot. Normal, binomial, and beta distributions, PDFs, CDFs, quantiles, mean and standard deviation, rules of probability, hypothesis testing, sampling, and of course Bayes theorem and its applications. The code examples are in R but if you don't know much R like me there is an appendix for that.A lot of the math is explored and Kurt makes it very intuitive, with concise but clear explanations of the key concepts. There are lots of funny, memorable examples, which if nothing else shows you how you can actually apply this thinking in real life. Because after all, Bayesian thinking is all about trying to think about the world probabilistically given the data we have, and maybe changing our mind.My only complaint was that the book was not long enough! I wanted more.

Terminei a primeira parte do livro e, mesmo para alguém sem background em estatística, tem sido uma leitura extremamente didática, divertida e interessante. Recomendo fortemente. Já havia tentado encarar outras obras sobre estatística bayesiana e esta sem dúvida tem sido a mais amigável.