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Each exact Gibbs block update is reversible and invariant
Statement
Let be a probability on a finite product state space and let be a supplied proper full conditional kernel for a nonempty block . The exact block-update kernel of Exact Gibbs block update is reversible with respect to and leaves invariant.
Facts & Assumptions
Given: The target probability, block, and supplied proper full conditional kernel in the Statement.
A proper full conditional kernel is pointwise a probability kernel and satisfies the block event identities against . Proper full conditional kernel.
The transition is . Exact Gibbs block update.
The update is a probability kernel. The Gibbs block update is a probability kernel.
A probability measure with a probability kernel defines a joint law with iterated-integral formula. A prior and model kernel define a joint law.
The product measure exists and is characterized by its rectangle evaluations. Finite products of probability kernels are probability kernels.
Reversibility is the identity for every pair of measurable events. Reversibility for a probability kernel.
Invariance means for every measurable . Invariant and stationary distribution for a Markov kernel.
Finite measures agreeing on a generating rectangle pi-system agree on the product sigma-algebra. Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system.
Proof
Given: The hypotheses in the Statement and facts [F1]–[F8].
The full conditional identity [F1] says that the target law in coordinates and the iterated law agree on coordinate rectangles. Both are probability measures, and rectangles generate the finite product sigma-algebra, so [F8] makes them equal on every product-measurable event. Integrating nonnegative simple functions and then increasing limits gives for every nonnegative product-measurable .
Let be the joint law of the current and updated states under initial law and transition , which exists by [F3]–[F4]. For arbitrary measurable , the update formula and step 1.1 give . All integrands are bounded measurable functions, so the iterated integrals are defined.
For each , the product measure is invariant under swapping its two coordinates: the product measure and its swapped image agree on every rectangle because , hence agree on the generated product sigma-algebra by [F8]. Applying this symmetry to the two inner factors in step 2.1 yields for arbitrary . By [F6], is reversible with respect to .
Set in the detailed-balance identity of step 3.1. Since , its left side is and its right side is . This is invariance by [F7], for every measurable .
Depends on
- Exact Gibbs block update
- The Gibbs block update is a probability kernel
- Proper full conditional kernel
- Reversibility for a probability kernel
- Invariant and stationary distribution for a Markov kernel
- Probability measures and probability spaces
- Finite products of probability kernels are probability kernels
- A prior and model kernel define a joint law
- Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system
Used by
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Sources
- Art B. Owen, Monte Carlo Theory, Methods and Examples, Chapter 12 (standard reference, not scraped)
- Gareth O. Roberts and Jeffrey S. Rosenthal, General State Space Markov Chains and MCMC Algorithms (standard reference, not scraped)