Bayes Theorem: The Ultimate Beginner's Guide to Bayes Theorem

The Perfect Book for Beginners Wanting to Visually Learn About Bayes Theorem Through Real Examples

What if you could quickly and easily learn Bayesian data analysis without complex textbooks and statistics classes?

Imagine being able to apply your newly learned theory to real life situations

Multi-time best selling IT & mathematics author, Arthur Taff, presents the perfect guide for any beginner. Bayesian data analysis can be difficult to learn, especially through textbooks and statistic classes at school. This book aims to solve that issue by presenting the theories in an easy-to-understand and visually intuitive way. This book contains a number of visual examples to build a basic understanding of Bayesian data analysis and then works to teach at a deeper level without the complexities you'd see in other similar books. Additionally, every example in this book has been solved using Excel.

In this book, you will get:

• A Basic Introduction to Bayes Theorem (with examples) - The initial introduction demonstrates how Bayesian data analysis works when you have a single new piece of data to update initial probabilities.
• Adding New Data & Updating Probabilities - Takes the above example and looks at what happens if we have multiple pieces of data instead of a single piece.
• Bayes Theorem Terminology - The formal names for the different parts of the Bayes Theorem equation, and how it all comes together for an easier overall understanding.
• How to Deal With Data Errors - In a real life situation, it is unlikely that your data will be error-free. This section shows you how to deal with those errors and still get accurate probability estimates.
• Arthur's personal email address for unlimited customer support if you have any questions
• And much, much more...

If you are a person that learns by example, especially visually, then this book is perfect for you It is a very important topic in a wide range of industries - so dive in to get a deep understanding

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