**Author**: Farhat Khan

**Publisher:**
**ISBN:** 9781690183891

**Size**: 69.13 MB

**Format:** PDF

**Category : **
**Languages : **en

**Pages : **73

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**Bayes Theorem books**, Bayes Theorem is a way of refreshing probability as you get new information. Mostly, you make an initial guess and then understand more data to improve it. Bayes Theorem, or Bayes Rule, has a numerous of real-world applicability, from estimating the risk of a heart attack to making recommendations on NetflixBut It Isn't That ComplicatedThis book is a short prelude to Bayes Theorem. It is only 15 pages long and is intended to show you how Bayes Theorem works as promptly as possible. The examples are deliberately kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions.If you want to learn Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.From spam filters to Netflix recommendations, to drug testing, Bayes Theorem (also known as Bayes Theory, Bayes Rule or Bayes Formula) is used through a considerable number of industries. The reason it is so useful is it provides a systematic way to update estimated probability as new data is found out.Bayesian data analysis is taught in many introductions to statistics classes. However, the problem is that it is not shown in a very intuitive way. This book, instead of focusing on the probability theory, focuses on building a deep understanding of how Bayesian statistics work. This book contains several visual examples to develop that understanding. Additionally, every instance in this book has been solved using Excel, and the Bayesian Excel file is available for free download to allow you to work the examples along with the book quickly.This book uses a building block approach to help the reader understand how Bayes Theorem works in real life, in addition to the probability theory. The topics covered areBayes Theorem Basic Example - A first example to show how Bayesian data analysis works when you have a single new piece of data to update initial probabilitiesUpdating Probabilities With Multiple Pieces Of New Data - What if instead of a single piece of data you have a lot of new measurements to update your probabilitiesBayes Theorem Terminology - The formal names for the different parts of the Bayes theorem equation, and how does relate to a more everyday understandingAre You A Winning Tennis Player? - Use the results from tennis matches to determine what your likely long term win rate isDealing With Errors In Your Data - In real life, you are unlikely to have the pure error-free data that you see in most examples. But if you want to use Bayesian data analysis to solve real-life problems, you need to account for the fact that some measurements will be wrong, or the data will be entered incorrectly, or there will be other errors. This section explains how to deal with those errors and still get accurate probability estimates.Historical Successes of Bayes Theorem - One of the most notable successes of Bayesian data analysis is the German Tank Problem. This was the problem of estimating how many tanks and other pieces of high-value equipment the enemy force had, using only a few pieces of captured equipment. Bayesian statistics solved this problem better than espionage, and this example shows how it was doneClassic Uses Of Bayes Theorem Today - A current famous application of Bayesian statistics is the drug testing problem. This problem asks how likely a person who got a positive result, for instance on a drug test or a test for the disease, is to have that disease or be a user of the drug, vs. having a false positive on the testIf you are a person that learns by example, this booklet might be a good fit for you. It is a critical topic in a wide range of industries - so dive in to get an intuitive understanding!