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Printable Handouts
Navigable Slide Index
- Introduction
- Simulation methods
- MCMC methods
- The Markov chain
- Ergodicity (1)
- Practical considerations
- Asymptotics
- Simulating from Bivariate normal
- Gibbs sampler (1)
- Gibbs sampler (2)
- Hammersley-Clifford theorem
- rbiNormGibbs (1)
- Intuition for dependence
- rbiNormGibbs (2)
- Ergodicity (2)
- Relative numerical efficiency (1)
- Relative numerical efficiency (2)
- General Gibbs sampler
- Different prior for Bayes regression
- Different posterior
- Different simulation strategy
- runiregGibbs (1)
- runiregGibbs (2)
- runiregGibbs (3)
- runiregGibbs (4)
- R session (1)
- R session (2)
- R session (3)
- Draws of beta
- Draws of sigma squared
- Summary
Topics Covered
- MCMC methods
- Ergodicity
- Gibbs sampler
- Relative numerical efficiency
- Bivariate normal example
- A different prior for Bayes regression
Talk Citation
Rossi, P. (2010, January 27). Introduction to MCMC methods and the Gibbs sampler [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved October 4, 2024, from https://doi.org/10.69645/YKDU7035.Export Citation (RIS)
Publication History
Introduction to MCMC methods and the Gibbs sampler
Published on January 27, 2010
28 min