Last updated 8/2020MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 933.92 MB | Duration: 1h 19m

Bayes' Theorem and Bayesian statistics from scratch - a bner's guide.

What you'll learn

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 bner's course introduces Bayesian statistics from scratch. It is appropriate both for those just bning their adventures in Bayesian statistics as well as those with experience who want to understand it more deeply.We b 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: What is probability?

Lecture 2 Bayesian vs Frequentist models of probability

Section 3: Conditional Probability

Lecture 3 Conditional Probability Intro

Lecture 4 Conditional Probability on Venn Diagrams

Lecture 5 Conditional Probability on Tree Diagrams

Lecture 6 Tree Diagram Example Question

Lecture 7 Conditional Probability and Normal Distributions

Lecture 8 Counter Intuitive Results with the Normal Distribution

Section 4: Bayes' Theorem

Lecture 9 Developing Bayes' Theorem part 1

Lecture 10 Developing Bayes' Theorem part 2

Lecture 11 Thomas Bayes' Puzzle

Lecture 12 A Bayesian Solution to the Puzzle

Lecture 13 Simulating a Solution

Lecture 14 Congratulations!

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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