How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Feynman–Kac path targets and normalizers
Definition
Assume AC and fix a finite horizon . For each , let be a standard Borel space. Let be a probability measure on , and for let be a probability kernel. A potential at time is a finite-valued nonnegative measurable function . Write with its finite-product sigma-algebra.
For each , let be the finite path law with ordered kernel integral , supplied by Ionescu–Tulcea (Ionescu-Tulcea construction of a Markov chain). The unnormalized Feynman–Kac path measure is Its normalizer is . Only when define the normalized path target . The sequence with its kernels and potentials is the Feynman–Kac flow.
State-space filtering kernels are conditional-law versions as in Filtering, predictive, and smoothing laws in a finite-horizon state-space model. A Feynman–Kac path target is not by itself an assertion that a pointwise conditional version has been selected at observation-null histories.
Remarks
Each coordinate projection from the finite product is measurable by The product sigma-algebra and its finite iterates. Thus is nonnegative product-measurable by composition and finite-product measurability (Composition with a Borel measurable outer map preserves measurability, Arithmetic and lattice operations preserve measurability whenever they are defined). Consequently, is a measure, possibly of infinite mass, by The indefinite integral of a nonnegative measurable function is a measure.
Depends on
- The Axiom of Choice
- Standard Borel spaces
- The product sigma-algebra and its finite iterates
- Measure kernel and probability kernel
- Ionescu-Tulcea construction of a Markov chain
- Composition with a Borel measurable outer map preserves measurability
- Arithmetic and lattice operations preserve measurability whenever they are defined
- The indefinite integral of a nonnegative measurable function is a measure
- Filtering, predictive, and smoothing laws in a finite-horizon state-space model
Used by
- Conditional particle filter for particle Gibbs Algorithm
- Full-resampling fixed-level SIR splitting with invariant mutations Algorithm
- Particle backward simulator Algorithm
- Particle marginal Metropolis–Hastings Algorithm
- Rao–Blackwellized particle filter Algorithm
- Sequential importance resampling for Feynman–Kac path targets Algorithm
- A particle proposal can miss positive target mass Counterexample
- Backward-simulation particle Gibbs extended target Definition
- Finite-horizon SMC CLT classes and variance recursions Definition
- Particle Gibbs extended target Definition
- Bootstrap and informed proposals on a two-state particle filter Example
- Feynman–Kac path measures satisfy the forward recursion Lemma
- Finite-budget stopped-kernel measurability and event normalizer Lemma
- Finite-horizon bootstrap particle filters are weakly consistent Lemma
- The Kalman filter is exact conditional on a nonlinear path Lemma
- Truncation and total-variation errors for stopped crossings Lemma
- Feynman–Kac path Radon–Nikodym weight Theorem
- Fixed-level SIR has an absolute O(L/N) mean-square bound Theorem
- The backward-simulation sweep is a Gibbs update Theorem
- The particle Gibbs extended target has the exact smoothing marginal Theorem
- The sequential particle estimator is unbiased for Feynman–Kac path integrals Theorem
Dependency tree · two levels
45 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Arnaud Doucet and Adam M. Johansen, A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later, §§3.3–3.5 (standard reference, not scraped)
- Simo Särkkä and Lennart Svensson, Bayesian Filtering and Smoothing, 2nd ed., §§11.2–11.5 (standard reference, not scraped)
- Nicolas Chopin, Central Limit Theorem for Sequential Monte Carlo Methods and its Application to Bayesian Inference, §§2.1–2.2 (standard reference, not scraped)