Bayesian Statistics: A Step-by-step Introduction

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Brian Greco

5:32:53

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1. Probability Basics

1. Introduction Video.mp4

01:32

2. Welcome.html

3. Probability, Complements, Venn Diagrams.mp4

06:42

4. Law of Total Probability.mp4

04:11

5. Conditional Probability.mp4

07:18

6. Multiplication Rule and Independence.mp4

07:14

7. Probability Quiz.html

8.1 Bayesian Homework 1.pdf

8. Probability Extra Practice Problems.html

2. Bayes Rule

1. Bayes Rule Introduction.mp4

07:16

2. Medical Testing Problem.mp4

06:37

3. Predictive Distributions and Flow Charts.mp4

04:47

4. Two Coins.mp4

06:04

5. Multiple Observations.mp4

05:18

6. Normalizing Constants and Proportionality.mp4

06:35

7. Bayes Rule Quiz.html

8.1 Bayes Rule Practice.pdf

8. Bayes Rule Extra Practice Problems.html

3. Continuous Priors and the Uniform Distribution

1. Continuous Priors and the Uniform Distribution.mp4

07:50

2. Likelihood and Random Variables.mp4

08:27

3. Posterior Distribution One Observation.mp4

07:08

4. Posterior Probability Calculation.mp4

02:30

5. Posterior Distribution Two Observations.mp4

06:14

6. CDFs and Inverse CDFs.mp4

09:54

7. Credible Intervals.mp4

07:25

8. Mean, Median, Mode.mp4

06:41

9. Point Estimates.mp4

08:56

10. Predictive Distribution.mp4

09:03

11. Continuous Uniform Distribution Quiz.html

4. Beta-Binomial Family

1. Binomial Random Variables.mp4

10:50

2. Prior The Beta Distribution.mp4

09:31

3. Finding prior and posterior probabilities with the Beta CDF.mp4

02:07

4. How do the hyperparameters change the prior.mp4

03:02

5. Posterior Distribution What are Conjugate Priors.mp4

13:01

6. Credible Intervals.mp4

05:29

7. Point Estimates.mp4

04:05

8. Predictive Distribution Beta-Binomial.mp4

08:37

9. Beta Distribution Quiz.html

10. Recap of Priors, Posteriors, Likelihoods, Predictive Distributions.mp4

03:35

11.1 bayesianbeta-binomialrexercise.zip

11.2 python betabinomial.zip

11. Example Beta-Binomial Problem with R.mp4

10:07

12.1 bayesianbeta-binomialrexercise.zip

12.2 python betabinomial.zip

12. Example Beta-Binomial Problem with Stan.mp4

07:47

13. Drawbacks of Conjugate Priors.mp4

03:21

5. Poisson - Gamma

1. Likelihood Poisson Distribution.mp4

08:37

2. Gamma Distribution and Choosing Hyperparameters.mp4

12:38

3. Posterior Distribution.mp4

07:14

4. Posterior Calculation Example 2.mp4

02:27

5. Credible Interval and Point Estimates.mp4

03:20

6. Predictive Distribution Negative Binomial.mp4

07:31

7. Poisson - Gamma Quiz.html

8.1 bayesianpoisson-gammarexample.zip

8.2 poissongammapython.zip

8. Example Poisson-Gamma Problem with R and Stan.mp4

09:43

6. Normal - Normal

1. Normal Distribution.mp4

07:29

2. Prior Normal Distribution and choosing hyperparameters.mp4

08:49

3.1 Normal Posterior Distribution Full Derivation.html

3. Posterior Distribution and Example.mp4

07:33

4. Credible Intervals and Point Estimates.mp4

02:57

5. Normal-Normal Quiz.html

6.1 bayesiannormalrexample.zip

6.2 python-normalnormal.zip

6. Example Normal-Normal Problem with R and Stan.mp4

11:20

7. More advanced models

1. Predictive distributions, unknown variance.mp4

07:30

2. Simple Linear Regression.mp4

08:54

3.1 bayesianregressionexample.zip

3.2 bayesianregressionpython.zip

3. Example Simple Linear Regression Problem with R and Stan.mp4

07:37

Description

A former Google data scientist helps you master the basics of Bayesian statistics, with examples in R and Stan

What You'll Learn?

Understand how Bayes' rule can be used to update beliefs
Use conjugate priors and likelihoods to model binary, count, and continuous data
Understand the concepts of prior distributions, posterior distributions, likelihood functions, and predictive distributions
Understand how statistical software can be used to compute and visualize information about your beliefs

Who is this for?

Current and aspiring data scientists and data analysts

Academics in the social, biological, and physical sciences

University students studying mathematics or statistics

Anybody who wants to learn to rigorously update their beliefs from data.

What You Need to Know?

Strong skills in basic algebra and arithmetic

Some knowledge of calculus is useful, but not required.

More details

Description
This course teaches the foundational material of statistics covered in an introductory college course, with a focus on mastering the basic components of any Bayesian model - the prior distribution and the likelihood, and how to find a posterior distribution, credible intervals, and predictive distributions.Â Along the way, you'll become more comfortable with probability in general and gain a new perspective on how to analyze data!

We start from scratch - no experience in Bayesian statistics is required.Â Students should have a strong grasp of basic algebra and arithmetic.Â R and RStudio, or Python, is required if you would like to run the optional coding sections

The course includes:
5.5 hours of video lectures
Interactive demonstrations using R and Stan (Python code is included too!)
Quizzes to check your understanding
Review assignments with solutions to practice what you have learned
You will learn:
The basic rules of probability
Bayes' rule, including common examples with medical testing and flipping coins
The terminology of different components of a Bayesian model:Â the prior distribution, posterior, likelihood, and predictive distribution
Conjugate priors
Credible intervals and Bayes estimators
Modeling binary data with the Bernoulli and Binomial Distribution, and the Beta distribution prior
Modeling count data with the Poisson Distribution, and the Gamma distribution prior
Modeling continuous data with the Normal Distribution, and the Normal distribution prior
An introduction to simple linear regression
This course is ideal for many types of students:
Anyone who wants to learn the foundations of Bayesian statistics and understand concepts like priors, posteriors and credible intervals
Data science and data analytics professionals who would like to refresh and expand their statistics knowledge
Academics in the social, biological, and physical sciences
This course is ideal for anyone, from beginners to seasoned professionals. It doesn't matter if you're just starting your journey in data science, looking to upgrade your existing skills, or simply have an interest in Bayesian statistics. My goal is to make Bayesian statistics accessible and understandable for all.
Who this course is for:
Current and aspiring data scientists and data analysts
Academics in the social, biological, and physical sciences
University students studying mathematics or statistics
Anybody who wants to learn to rigorously update their beliefs from data.

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Statistics

Brian Greco

Instructor's Courses

I am a statistician, data scientist, and teacher. I've worked as a data scientist at Google, where I analyzed data from over 1 billion users, and I've created new methods for analyzing genetic sequencing data. But most importantly, I love helping others learn statistics and have been teaching at top universities and online for over ten years. My goal is to help you obtain a deep understanding of statistics and to make it as easy as possible!

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