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

I am about a quarter of the way through the book and I’ve learned a ton. The author uses great examples to clarify key concepts. I actually learned some of the material in the book in college but didn’t fully understand how or when to apply the techniques until now. I would highly recommend as a supplement for any stats student or professional looking to refresh on Bayesian statistics. Huge thumbs up for being an easy read as well.

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 don't have a strong background in math, but I'm not math-phobic. This is clearly written and explains the logic underlying the formulas very well. But I quickly got in over my head. The author urges (and makes possible) downloading a program for tedious calculations. So the book doesn't quite stand alone for someone who doesn't already have some supporting computational software. I may not download the software, but I don't begrudge the money for the book.

I am a physician had to pass biostats when I did a degree in infectious diseases through LSHTMAt that time used a book by Martin Blount-frequentist stats and also used the book by Donald BerryAlways felt Bayesian stats better for medicineUsed to live in Royal Tunbridge Wells and worked in a clinic right next door to a house bearing a plaque which read’In this house lived Reverend Thomas Bayes”I am sure the Reverend would be pleased with your book.It is excellent as an introductory course and as a warm up for those who have some experience with Bayesian statisticsIt is one of the best

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!

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.

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.

Just getting started. Love it already!

As someone who did a detailed statistics course at University 50 years ago I bought this book in the hope of demystifying the adjective "Bayesian" which, like blockchain and AI, seems to be an essential term to be inserted into any self-respecting scientific proposal these days. The author does that demystification extremely well and demonstrates it very effectively using both common visual aids, like Lego bricks...and also formulae which can be a trifle intimidating if you are not comfortable with mathematical shorthand.To stress, it is not for the fainthearted - there is no way you could ever really understand Bayesian statistics, or indeed pre-Bayesian statistics (which the book also covers very well as the essential introduction) - without working through this level of detail. Do be prepared therefore to do some serious thinking when reading it, unless the topic is already very familiar to you.

I have read quite a few statistics books, particularly about Bayesian stats but no book has ever come close to this book! This book assumes no prior knowledge but rapidly ramps you up from basic concepts to more advanced problems and concepts. It is nothing short of a brilliant introduction and is highly recommended.

I have a high school stats exposures (frequency, distributions, tests etc) but bought this to stay abreast of my son's knowledge as he progresses through university. The material handles the introductory concepts very well. Liked his appendix on calculus (for non-mathematicians). Clearly the author has a gift for presenting difficult stuff in a lighthearted but meaningful way.

The author builds intuition before presenting mathematics. The book is not cluttered with proofs but instead uses a few results with R to illustrate the practical importance of a Bayesian approach to problems. You do not need to know R as there is a good appendix for beginners. Also you do not have to use R; I did the entire book with Python + SciPy and had no issue.

Extremely enjoyable, simple but profound at the same time. A must read for anyone interested in the heart of statistics.