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. 29 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. 29 hours to complete

English

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

Try Coursera for Business

Instructor

Instructor rating

4.94/5 (71 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 Rating94%(2,737 ratings)

Week1

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 introduction5mObjectives7mModeling process8mComponents of Bayesian models8mModel specification7mPosterior derivation9mNon-conjugate models7mMonte Carlo integration9mMonte Carlo error and marginalization6mComputing examples15mComputing Monte Carlo error13m

4 readings

Module 1 assignments and materials3mReference: Common probability distributionsCode for Lesson 3Markov chains20m

4 practice exercises

Lesson 120mLesson 225mLesson 330mMarkov chains20m

Week2

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

Algorithm9mDemonstration10mRandom walk example, Part 112mRandom walk example, Part 216mDownload, install, setup3mModel writing, running, and post-processing12mMultiple parameter sampling and full conditional distributions8mConditionally conjugate prior example with Normal likelihood10mComputing example with Normal likelihood16mTrace plots, autocorrelation17mMultiple chains, burn-in, Gelman-Rubin diagnostic8m

7 readings

Module 2 assignments and materials3mCode for Lesson 4Alternative MCMC software10mCode from JAGS introductionCode for Lesson 510mAutocorrelation10mCode for Lesson 6

4 practice exercises

Lesson 420mLesson 530mLesson 620mMCMC45m

Week3

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 regression8mSetup in R9mJAGS model (linear regression)12mModel checking17mAlternative models10mDeviance information criterion (DIC)4mIntroduction to ANOVA10mOne way model using JAGS18mIntroduction to logistic regression6mJAGS model (logistic regression)18mPrediction15m

5 readings

Module 3 assignments and materials3mCode for Lesson 7Code for Lesson 8Code for Lesson 9Multiple factor ANOVA20m

5 practice exercises

Lesson 7 Part A30mLesson 7 Part B30mLesson 830mLesson 945mCommon models and multiple factor ANOVA30m

Week4

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 regression3mJAGS model (Poisson regression)17mPredictive distributions11mCorrelated data8mPrior predictive simulation10mJAGS model and model checking (hierarchical modeling)13mPosterior predictive simulation8mLinear regression example7mLinear regression example in JAGS10mMixture model in JAGS13m

7 readings

Module 4 assignments and materials3mPrior sensitivity analysis20mCode for Lesson 10Normal hierarchical model20mApplications of hierarchical modeling10mCode and data for Lesson 11Mixture model introduction, data, and code20m

4 practice exercises

Lesson 1040mLesson 11 Part A40mLesson 11 Part B30mPredictive distributions and mixture models30m

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TOP REVIEWS FROM BAYESIAN STATISTICS: TECHNIQUES AND MODELS

by RNApr 26, 2020

Fantastic course that I was able to immediately incorporate into my work. Great mix of theory and hands on coding!

by MSAug 19, 2020

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

by ATMay 1, 2018

Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course

by VVMar 20, 2018

Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.

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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