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.
Finite-horizon state-space kernel law exists and is unique
Statement
Assume AC. Fix a finite horizon , standard-Borel spaces and , an initial probability measure on , and transition and observation probability kernels and for , as in General finite-horizon state-space models by kernels. On the interleaved finite product space there is a unique probability measure whose rectangle probabilities are the ordered iterated kernel integrals The product notation means successive integration in the displayed order. For , the assertion is the initial law on .
Facts & Assumptions
Given: Assume AC and the finite-horizon spaces, initial law, and kernels in the Statement.
AC asserts that every family of nonempty sets has a choice function. (The Axiom of Choice)
A finite-horizon model consists of standard-Borel state and observation spaces, an initial law, transition probability kernels, and observation probability kernels. (General finite-horizon state-space models by kernels)
A probability kernel has probability-measure sections and measurable evaluations. (Measure kernel and probability kernel)
Under AC, an initial probability measure and a sequence of probability kernels on measurable spaces give a unique path-space probability whose finite-prefix laws are their iterated integrals. (Ionescu-Tulcea construction of a Markov chain)
Every finite product of standard-Borel spaces, with the finite-product sigma-algebra, is standard Borel. (Finite products of standard Borel spaces are standard Borel)
The finite-product sigma-algebra is generated by its measurable rectangles. (The product sigma-algebra and its finite iterates)
Proof
Given: The hypotheses in the Statement.
If , then and is the unique asserted law. For , interleave the coordinate spaces as , , and for . At the transition to , define the lifted kernel by , where selects the coordinate of the preceding history ; at the next transition define , where selects the newly adjoined coordinate. These projections are measurable because inverse images of measurable sets are product cylinders. Each lifted section is a probability measure, and each evaluation is measurable by composition, so [F2] makes every lift a probability kernel. After coordinate , append singleton spaces with their unit point-mass kernels.
By [A1] and [F3], Ionescu–Tulcea gives a unique probability measure on the countable interleaved product with these lifted kernels. Its marginal on the first coordinates is a probability measure on ; the prescribed prefix formula is exactly the displayed iterated integral. The finite product is standard Borel by [F4].
Suppose is another probability measure on with the same displayed rectangle probabilities. Append the singleton tail to the coordinate tuple by a measurable map and push forward to the countable product. For any shorter prefix, set all later coordinate sets in the rectangle formula to their whole spaces; the later probability kernels integrate to one, so each shorter marginal is the prescribed iterated integral. Prefixes after add only deterministic singleton coordinates. Ionescu–Tulcea uniqueness [F3] identifies this extension with the measure in step 2.1, so their finite-prefix marginals agree and equals that marginal. For , the formula already fixes .
Depends on
Used by
- Filtering, predictive, and smoothing laws in a finite-horizon state-space model Definition
- A scalar Gaussian state-space update by completing the square Example
- A two-state hidden Markov filter and smoother Example
- An impossible observation has zero evidence and only a filler filter value Example
- Same state dynamics with distinct observation kernels Example
- Singular state covariance with positive innovation covariance Example
- Bayes factorization of the smoothing backward kernel Lemma
- Exact state-space filtering recursion under dominated observations Theorem
- Filtering and finite-horizon smoothing laws exist Theorem
- The finite linear-Gaussian state-space path is jointly Gaussian Theorem
Cited to discharge well-definedness by General finite-horizon state-space models by kernels.
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
- Nicolai Meinshausen, Time Series Analysis: Notes for the course HS 2022 (standard reference, not scraped)
- Dewei Wang, STAT 720 Time Series Analysis, Spring 2015 lecture notes (standard reference, not scraped)