Gibbs sampling as a special case of Metropolis-Hastings
Gibbs sampling can be seen as a special case of the Metropolis-Hastings algorithm where the transition operators are chosen such that the acceptance probability is 1.
This concept has the prerequisites:
Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: page 544 of section 11.3
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: section 24.3.2, pages 849-850
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