Bayesian Statistics: Techniques and Models
- 4.8
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Course Summary
Learn the concepts of Markov Chain Monte Carlo (MCMC) and Bayesian Statistics in this course. Discover how to implement MCMC algorithms to solve statistical problems and make informed decisions.Key Learning Points
- Understand the basic concepts of Markov Chain Monte Carlo (MCMC) and Bayesian Statistics
- Learn how to use MCMC algorithms to solve statistical problems and make informed decisions
- Explore the practical applications of MCMC and Bayesian Statistics in various fields
Job Positions & Salaries of people who have taken this course might have
- USA: $113,000
- India: ₹1,200,000
- Spain: €39,000
- USA: $113,000
- India: ₹1,200,000
- Spain: €39,000
- USA: $95,000
- India: ₹1,000,000
- Spain: €32,000
- USA: $113,000
- India: ₹1,200,000
- Spain: €39,000
- USA: $95,000
- India: ₹1,000,000
- Spain: €32,000
- USA: $87,000
- India: ₹900,000
- Spain: €29,000
Related Topics for further study
Learning Outcomes
- Understand the concepts of MCMC and Bayesian Statistics
- Implement MCMC algorithms to solve statistical problems
- Apply MCMC and Bayesian Statistics in various fields
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of probability and statistics
- Familiarity with programming languages like R or Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Bayesian Statistics: Techniques and Models
- Data Science: Probability
Related Education Paths
Notable People in This Field
- Andrew Gelman
- Christian Robert
Related Books
Description
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.
Outline
- Statistical modeling and Monte Carlo estimation
- Course introduction
- Objectives
- Modeling process
- Components of Bayesian models
- Model specification
- Posterior derivation
- Non-conjugate models
- Monte Carlo integration
- Monte Carlo error and marginalization
- Computing examples
- Computing Monte Carlo error
- Module 1 assignments and materials
- Markov chains
- Lesson 1
- Lesson 2
- Lesson 3
- Markov chains
- Markov chain Monte Carlo (MCMC)
- Algorithm
- Demonstration
- Random walk example, Part 1
- Random walk example, Part 2
- Download, install, setup
- Model writing, running, and post-processing
- Multiple parameter sampling and full conditional distributions
- Conditionally conjugate prior example with Normal likelihood
- Computing example with Normal likelihood
- Trace plots, autocorrelation
- Multiple chains, burn-in, Gelman-Rubin diagnostic
- Module 2 assignments and materials
- Alternative MCMC software
- Code for Lesson 5
- Autocorrelation
- Lesson 4
- Lesson 5
- Lesson 6
- MCMC
- Common statistical models
- Introduction to linear regression
- Setup in R
- JAGS model (linear regression)
- Model checking
- Alternative models
- Deviance information criterion (DIC)
- Introduction to ANOVA
- One way model using JAGS
- Introduction to logistic regression
- JAGS model (logistic regression)
- Prediction
- Module 3 assignments and materials
- Multiple factor ANOVA
- Lesson 7 Part A
- Lesson 7 Part B
- Lesson 8
- Lesson 9
- Common models and multiple factor ANOVA
- Count data and hierarchical modeling
- Introduction to Poisson regression
- JAGS model (Poisson regression)
- Predictive distributions
- Correlated data
- Prior predictive simulation
- JAGS model and model checking (hierarchical modeling)
- Posterior predictive simulation
- Linear regression example
- Linear regression example in JAGS
- Mixture model in JAGS
- Module 4 assignments and materials
- Prior sensitivity analysis
- Normal hierarchical model
- Applications of hierarchical modeling
- Mixture model introduction, data, and code
- Lesson 10
- Lesson 11 Part A
- Lesson 11 Part B
- Predictive distributions and mixture models
- Capstone project
- Course conclusion
- Further reading and acknowledgements
Summary of User Reviews
Discover the power of MCMC and Bayesian statistics in this comprehensive Coursera course. Learners praised the real-world applications and practical exercises that help solidify understanding.Key Aspect Users Liked About This Course
Real-world applications and practical exercisesPros from User Reviews
- Clear and concise explanations
- Great for beginners and intermediate learners
- Engaging and interactive content
- In-depth coverage of MCMC and Bayesian statistics
- Well-organized course structure
Cons from User Reviews
- Some lectures can be too fast-paced
- Not enough emphasis on the mathematical fundamentals
- Limited access to the instructor
- Some technical glitches in the online platform
- No certificate of completion for the free version
Matthew HeinerTop Instructor
4.8 Raiting
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