This course is part of the Bayesian Statistics Specialization

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Matthew Heiner

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Bayesian Statistics SpecializationUniversity of California, Santa Cruz

About this Course

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This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.

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Course 2 of 5 in the

Bayesian Statistics Specialization

Intermediate Level

Approx. 30 hours to complete

English

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Skills you will gain

Gibbs Sampling
Bayesian Statistics
Bayesian Inference
R Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

Bayesian Statistics Specialization

Intermediate Level

Approx. 30 hours to complete

English

Could your company benefit from training employees on in-demand skills?

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Instructor

Instructor rating

4.95/5 (69 Ratings)

Matthew Heiner
Top Instructor

Offered by

University of California, Santa Cruz

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Syllabus - What you will learn from this course

Content Rating93%(2,697 ratings)

Week

Week 1

4 hours to complete

Statistical modeling and Monte Carlo estimation

4 hours to complete

11 videos (Total 99 min), 4 readings, 4 quizzes

11 videos

Course introduction5m

Objectives7m

Modeling process8m

Components of Bayesian models8m

Model specification7m

Posterior derivation9m

Non-conjugate models7m

Monte Carlo integration9m

Monte Carlo error and marginalization6m

Computing examples15m

Computing Monte Carlo error13m

4 readings

Module 1 assignments and materials3m

Reference: Common probability distributions

Code for Lesson 3

Markov chains20m

4 practice exercises

Lesson 120m

Lesson 225m

Lesson 330m

Markov chains20m

Week

Week 2

5 hours to complete

Markov chain Monte Carlo (MCMC)

5 hours to complete

11 videos (Total 129 min), 7 readings, 4 quizzes

11 videos

Algorithm9m

Demonstration10m

Random walk example, Part 112m

Random walk example, Part 216m

Download, install, setup3m

Model writing, running, and post-processing12m

Multiple parameter sampling and full conditional distributions8m

Conditionally conjugate prior example with Normal likelihood10m

Computing example with Normal likelihood16m

Trace plots, autocorrelation17m

Multiple chains, burn-in, Gelman-Rubin diagnostic8m

7 readings

Module 2 assignments and materials3m

Code for Lesson 4

Alternative MCMC software10m

Code from JAGS introduction

Code for Lesson 510m

Autocorrelation10m

Code for Lesson 6

4 practice exercises

Lesson 420m

Lesson 530m

Lesson 620m

MCMC45m

Week

Week 3

6 hours to complete

Common statistical models

6 hours to complete

11 videos (Total 131 min), 5 readings, 5 quizzes

11 videos

Introduction to linear regression8m

Setup in R9m

JAGS model (linear regression)12m

Model checking17m

Alternative models10m

Deviance information criterion (DIC)4m

Introduction to ANOVA10m

One way model using JAGS18m

Introduction to logistic regression6m

JAGS model (logistic regression)18m

Prediction15m

5 readings

Module 3 assignments and materials3m

Code for Lesson 7

Code for Lesson 8

Code for Lesson 9

Multiple factor ANOVA20m

5 practice exercises

Lesson 7 Part A30m

Lesson 7 Part B30m

Lesson 830m

Lesson 945m

Common models and multiple factor ANOVA30m

Week

Week 4

5 hours to complete

Count data and hierarchical modeling

5 hours to complete

10 videos (Total 106 min), 7 readings, 4 quizzes

10 videos

Introduction to Poisson regression3m

JAGS model (Poisson regression)17m

Predictive distributions11m

Correlated data8m

Prior predictive simulation10m

JAGS model and model checking (hierarchical modeling)13m

Posterior predictive simulation8m

Linear regression example7m

Linear regression example in JAGS10m

Mixture model in JAGS13m

7 readings

Module 4 assignments and materials3m

Prior sensitivity analysis20m

Code for Lesson 10

Normal hierarchical model20m

Applications of hierarchical modeling10m

Code and data for Lesson 11

Mixture model introduction, data, and code20m

4 practice exercises

Lesson 1040m

Lesson 11 Part A40m

Lesson 11 Part B30m

Predictive distributions and mixture models30m

Reviews

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TOP REVIEWS FROM BAYESIAN STATISTICS: TECHNIQUES AND MODELS

by TONov 23, 2020

I learned a lot about MCMC. This course is taught using R, but I personally was also working on it in python at the same time. I would love to try a higher class. Thank you!

by JHOct 31, 2017

This course is excellent! The material is very very interesting, the videos are of high quality and the quizzes and project really helps you getting it together. I really enjoyed it!!!

by MSAug 19, 2020

Excellent course for introducing yourself to Monte Carlo Methods applied to Bayesian statistics. Highly recommended!

by SKNov 16, 2019

One of the best designed courses. The material and videos are very precise and informative. The quiz questions and assignment are very enjoyable. Thank you !

View all reviews

About the Bayesian Statistics Specialization

This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Bayesian Statistics: Techniques and Models

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