Introduction To Bayesian Statistics

Bayes' Theorem
Bayesian statistics
Conditional probability
An understanding of subjective approaches to probability
Using Venn and Tree diagrams to model probability problems
Requirements
An understanding of probability basics.
Description
Bayesian statistics is used in many different areas, from machine learning, to data analysis, to sports betting and more. It's even been used by bounty hunters to track down shipwrecks full of gold!This beginner's course introduces Bayesian statistics from scratch. It is appropriate both for those just beginning their adventures in Bayesian statistics as well as those with experience who want to understand it more deeply.We begin by figuring out what probability even means, in order to distinguish the Bayesian approach from the Frequentist approach.Next we look at conditional probability, and derive what we call the "Baby Bayes' Theorem", and then apply this to a number of scenarios, including Venn diagram, tree diagram and normal distribution questions.We then derive Bayes' Theorem itself with the use of two very famous counter-intuitive examples.We then finish by looking at the puzzle that Thomas Bayes' posed more than 250 years ago, and see how Bayes' Theorem, along with a little calculus, can solve it for us.
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.
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