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The pCN accept-reject kernel preserves the posterior
Statement
Assume the Axiom of Choice and the inputs of the prior-preserving Crank–Nicolson algorithm: a separable real Hilbert space , centered Borel Gaussian reference probability , full-mass Borel domain , Borel potential , positive finite evidence , and scale . Let be the normalized target on obtained by extending by off , and let be the algorithm's accept-reject transition. Then is reversible with respect to : its joint flow is unchanged by for every Borel . Consequently, is invariant: This conclusion gives invariance only; it does not assert uniqueness, convergence, mixing, an effective-sample-size bound, mesh-independent cost, or correctness of an approximate forward solver.
Statement references
The pCN target, total potential extension, proposal, and accept-reject transition.
Reversibility means symmetry of the joint flow measure under coordinate swap.
A symmetric joint flow implies invariance of its probability target.
The Gaussian pCN proposal pair law under the reference probability is symmetric.
Facts & Assumptions
Given: The algorithm inputs in the Statement.
The Axiom of Choice and the algorithm's full-mass domain, potential extension, evidence, target density, proposal, and acceptance convention. The Axiom of Choice Prior-preserving Crank–Nicolson sampler
The proposal rule and accept-reject rule are Borel probability kernels; for each , where . The pCN accept-reject procedure defines a probability kernel
If , then , when , and if either potential is infinite. Prior-preserving Crank–Nicolson sampler Misfit evidence and the potential posterior law
For independent with law and , the joint Borel law of is symmetric under coordinate swap; on the separable Hilbert product, the Borel sigma-algebra equals the product sigma-algebra. The pCN Gaussian reference pair law is symmetric Borel sigma-algebra of a product of second-countable spaces
A prior and probability kernel define a joint flow probability measure with the rectangle formula and nonnegative iterated-integral identity. A prior and model kernel define a joint law Measure kernel and probability kernel Probability measures and probability spaces
Equality of probability laws is preserved under measurable changes of variables, and integration against a law equals expectation of the corresponding random-element function. Law or distribution of a random element Change of variables for expectation
The product of two probability measures exists and has the rectangle formula; the rectangle criterion then makes its coordinate projections independent random elements. For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique Independent random elements are characterized by finite rectangle probabilities Independent random elements The product sigma-algebra and its finite iterates
A probability kernel is reversible with respect to a probability measure when its joint flow equals its coordinate transpose; this definition requires no density. General-state reversibility by symmetric flow measure
Reversibility with respect to a probability measure implies its invariance. General-state reversibility implies invariance
Since is separable, norm balls centered at a countable dense subset with positive rational radii form a countable base of its induced metric topology: given with open, choose with , then a dense point with and a positive rational with ; the triangle inequality gives . Hence is second-countable, and the Borel sigma-algebra of equals . Separability: the existence of an at most countable dense subset Second countability: an at most countable basis for the topology A product of two at most countable sets is at most countable is countably infinite The rationals embed densely in the reals Every subset of an at most countable set is at most countable Hilbert space A norm on a real vector space, the induced metric, and the dictionary with the metric axioms Open ball, closed ball and sphere in a metric space The metric topology: a set is open when every one of its points has a ball around it inside the set; closed means open complement Arbitrary unions and finite intersections of open sets are open, open balls are open and closed balls are closed Borel sigma-algebra of a product of second-countable spaces
Continuous maps have Borel preimages of Borel sets. A continuous map has Borel preimages of Borel sets
Proof
Given: The hypotheses and notation in the Statement.
AC is assumed as recorded in [A1]. By [F9], is second-countable and the Borel sigma-algebra of is the product sigma-algebra. Put and let be the joint flow of the reference probability and proposal kernel. It exists by [F4]. Take the product probability space and its coordinate random elements ; [F6] gives their independence and common law . For every nonnegative product-Borel , the iterated-integral identity in [F4] and [F1] give where the proposal-row pushforward and the change-of-variables identity [F5] identify the flow integral with expectation under , and the product joint law [F6] together with the measurable proposal map identifies that expectation with the displayed one. Thus is the law in [F3], so is symmetric.
Define and . For every , if both potential values are finite, then If either value is , the algorithm sets and , so the identity still holds. In particular, is a symmetric nonnegative Borel function.
Set . The diagonal map is continuous from into the product topology on , so by [F10] it is Borel. Thus is measurable for every Borel . By the diagonal term in [F1] and the joint-flow formula [F4], the rejection part of the target joint flow has mass on It is symmetric because for every : for every Borel , .
For Borel , the accepted part of the target joint flow is The equality follows from the target density in [F2], identity 1.2, and the iterated-integral formula [F4]. Since is symmetric by step 1.1 and , this accepted measure is symmetric. Indeed, for , the change-of-variables identity [F5] gives Equality for every Borel is symmetry under .
By the decomposition of in [F1] and the joint-flow identity [F4], . The accepted and rejection pieces are symmetric by steps 2.1 and 1.3, hence is symmetric. By [F7], is reversible with respect to .
The hypotheses of [F8] hold: [F1] gives a probability kernel and the algorithm gives the probability measure . Therefore [F8] yields for every Borel . The proof uses only the symmetric-flow identity; it establishes no uniqueness or convergence claim.
Depends on
- Prior-preserving Crank–Nicolson sampler
- The Axiom of Choice
- General-state reversibility by symmetric flow measure
- Hilbert space
- Misfit evidence and the potential posterior law
- A Borel prior concentrated on a forward-map domain
- Law or distribution of a random element
- Open ball, closed ball and sphere in a metric space
- The metric topology: a set is open when every one of its points has a ball around it inside the set; closed means open complement
- A norm on a real vector space, the induced metric, and the dictionary with the metric axioms
- Measure kernel and probability kernel
- Probability measures and probability spaces
- Separability: the existence of an at most countable dense subset
- Second countability: an at most countable basis for the topology
- The product sigma-algebra and its finite iterates
- Independent random elements
- Borel sigma-algebra of a product of second-countable spaces
- The pCN Gaussian reference pair law is symmetric
- The pCN accept-reject procedure defines a probability kernel
- Every subset of an at most countable set is at most countable
- A prior and model kernel define a joint law
- Change of variables for expectation
- A continuous map has Borel preimages of Borel sets
- General-state reversibility implies invariance
- Independent random elements have product joint law
- Arbitrary unions and finite intersections of open sets are open, open balls are open and closed balls are closed
- Independent random elements are characterized by finite rectangle probabilities
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- A product of two at most countable sets is at most countable
- $\mathbb{Q}$ is countably infinite
- The rationals embed densely in the reals
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Sources
- S. L. Cotter, G. O. Roberts, A. M. Stuart, and D. White, MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster, Statistical Science 28(3) (2013) (standard reference, not scraped)
- Yury Korolev and Jonas Latz, Inverse Problems: Lecture Notes, Michaelmas Term 2020 (University of Cambridge, 18 May 2021) (standard reference, not scraped)
- G. O. Roberts and J. S. Rosenthal, General State Space Markov Chains and MCMC Algorithms, Probability Surveys 1 (2004) (standard reference, not scraped)