Glossary
- Gibbs Sampling:
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One of a number of computational methods collectively known as Markov Chain Monte Carlo (MCMC) methods. In simulating a Markov Chain, Gibbs sampling can be viewed as a special case of the Metropolis-Hastings algorithm. In statistical practice, the terminology Gibbs sampling most often refers to MCMC computations based on conditional distributions for the purpose of drawing inferences in multi-parameter Bayesian models.
- Bayesian Inference:
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Consider the parameter θ of a probability model as a random variable with the prior density function p(θ). The choice of a prior distribution may be based on previous experience or personal opinion. Then Bayesian inference combines information in the observed data x with information provided by the prior distribution to obtain a posterior distribution p(θ|x). The parameter θ may be a vector.
The likelihood function p(x|θ) is defined (up to a constant multiple) as the joint density function of the data x, now viewed as a...
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Trumbo, B., Suess, E. (2014). Gibbs Sampling. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_146
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