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Printable Handouts
Navigable Slide Index
- Introduction
- Review of Markov chains
- Transition matrix
- Stationary distribution (1)
- Stationary distribution (2)
- Time reversible chain (1)
- Time reversible chain (2)
- Stationary and time reversibility
- Example
- Principle number 1
- Metropolis methods
- Discrete case: Metropolis-Hastings algorithm (1)
- Discrete case: Metropolis-Hastings algorithm (2)
- Metropolis-Hastings algorithm - why do we repeat
- Time reversible with respect to pie
- Metropolis-Hastings algorithm example
- Continuous Metropolis-Hastings
- Principle number 2
- Independence chain
- Random walk (rw) chains
- Independence vs. rw chains
- Choosing a step size for the rw chain (1)
- Relative numerical efficiency (1)
- Relative numerical efficiency (2)
- Choosing a step size for the rw chain (2)
- The Gibbs sampler (1)
- The Gibbs sampler (2)
- Principle number 3
- Logit model
- Logit model-Hessian
- Logit model MCMC algorithms
- Scaling rw Metropolis
- Comparison of indep / rw Metropolis
- rmnlIndepMetrop (1)
- rmnlIndepMetrop (2)
- Time series of all the draws of beta
- Quantile regression
- Asymmetric Laplace distribution
- Bayesian quantile regression
- Quantile R code
- Summary
Topics Covered
- Review of Markov chains
- Stationary distribution
- Time reversible chains
- Metropolis methods
- Discrete case: Metropolis-Hastings algorithm
- Independence chains
- Random walk chains
- Relative numerical efficiency
- The Gibbs sampler
- Logit model
- Scaling rw metropolis
- Quantile regression
- Asymmetric Laplace distribution
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Talk Citation
Allenby, G.M. (2010, January 27). Metropolis algorithms, logit and quantile regression estimation [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved November 14, 2024, from https://doi.org/10.69645/MEAH2474.Export Citation (RIS)
Publication History
Metropolis algorithms, logit and quantile regression estimation
Published on January 27, 2010
61 min