Introduction to Bayesian Statistics
From machine learning to data analysis to sports betting and more, Bayesian statistics is used in a wide range of applications. Bounty hunters have even employed it to find gold-laden shipwrecks!
Bayesian statistics are presented in its entirety in this introductory course. Both those just getting started with Bayesian statistics and those with some background who want to learn more about it thoroughly should find it useful. In order to differentiate between the Frequentist approach and the Bayesian approach, you must first define what probability actually is.
You will then examine conditional probability, derive what is known as the "Baby Bayes' Theorem," and apply it to a variety of situations, such as Venn diagram, tree diagram, and normal distribution problems.
Following that, you will use two extremely well-known counterintuitive examples to derive Bayes' Theorem itself. After that, you'll look at the riddle Thomas Bayes posed more than 250 years ago and see how Bayes' Theorem and a little calculus can help you solve it.
Who this course is for:
- People who want to understand Bayes' Theorem intuitively and deeply.
- People interested in probability.
- Data scientists looking to develop their understanding of probability theory.
- Students interested in deepening their understanding of probability.
Requirements
- An understanding of probability basics.
Course ratings: 4.8/5
Enroll here: https://www.udemy.com/course/introduction-to-bayesian-statistics/
