- Gibbs Sampling
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 multiparameter Bayesian models
- Bayesian Inference
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 function...
KeywordsMarkov Chain Posterior Distribution Markov Chain Monte Carlo Prior Distribution Bayesian Inference
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