A full conditional distribution is the conditional law of one component of a joint model given all the other components and the observed data. A Gibbs sampler updates components by drawing from these laws.
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- Blocked Gibbs sampler
- Data augmentation
- Detailed balance of a random-scan Gibbs sampler
- Gamma–Poisson hierarchical model
- Gaussian change-point Gibbs updates
- Gaussian change-point posterior with proper priors
- Gibbs sampling for a finite hidden spin field
- Gibbs updates for a Poisson count change point
- Latent-normal Gibbs sampler for probit regression
- Local likelihood factors in a hidden spin field
- Metropolis-within-Gibbs algorithm
- Past exam of the mathematics course of the University of Cambridge / 2001 / iii / Paper 33 / 2 / i / Solution
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- Past exam of the mathematics course of the University of Cambridge / 2009 / iii / Paper 40 / 5 / b / Solution
- Past exam of the mathematics course of the University of Cambridge / 2009 / iii / Paper 40 / 6 / d / Solution
- Past exam of the mathematics course of the University of Cambridge / 2010 / iii / Paper 33 / 5 / Solution
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- Poisson–exponential posterior shrinkage formula
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