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.
General finite-horizon state-space models by kernels
Definition
Assume AC (The Axiom of Choice) and fix a finite horizon . For , let be a standard-Borel state space; for , let be a standard-Borel observation space (Standard Borel spaces), meaning a measurable space bimeasurably isomorphic to a Polish space. A probability kernel has probability-measure sections and measurable evaluations as in Measure kernel and probability kernel. Specify an initial probability measure on (a measure of total mass one, as in Probability measures and probability spaces), transition probability kernels from to , and observation probability kernels from to , for .
The trajectory space, with the finite product sigma-algebra generated by measurable coordinate rectangles (The product sigma-algebra and its finite iterates), is with . Its finite-horizon law is the unique probability law whose ordered iterated kernel integral is The product notation means successive integration in the displayed order; it does not assert the existence of densities. Existence and uniqueness under AC are proved in Finite-horizon state-space kernel law exists and is unique ↗. Every finite product trajectory space is standard Borel by Finite products of standard Borel spaces are standard Borel.
The initial and transition laws specify the state evolution, while each specifies the observation law given the current state in this model. State evolution by itself therefore does not specify a joint state-observation law. No density or time-homogeneity assumption is imposed, and no strict stationarity assumption (shift invariance of all finite-dimensional laws) is required (Strict and weak stationarity for integer-indexed time series). This is a single law, not a parameter-indexed family. The model contract records a proposed law and makes no claim that it adequately describes an application (Scientific statistical model contracts).
Depends on
- The Axiom of Choice
- Scientific statistical model contracts
- Strict and weak stationarity for integer-indexed time series
- Standard Borel spaces
- The product sigma-algebra and its finite iterates
- Finite products of standard Borel spaces are standard Borel
- Measure kernel and probability kernel
- Probability measures and probability spaces
- Ionescu-Tulcea construction of a Markov chain
Used by
- Recursive Bayesian filtering under dominated observations Algorithm
- Conditionally linear-Gaussian state-space model Definition
- Filtering, predictive, and smoothing laws in a finite-horizon state-space model Definition
- Linear-Gaussian state-space models with independent noises 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
- Exact state-space filtering recursion under dominated observations Theorem
- Filtering and finite-horizon smoothing laws exist Theorem
- Finite-horizon state-space kernel law exists and is unique Theorem
- The finite linear-Gaussian state-space path is jointly Gaussian Theorem
Dependency tree · two levels
55 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)