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Finite-horizon state-space kernel law exists and is unique

Statement

Assume AC. Fix a finite horizon T≥0, standard-Borel spaces E0,…,ET and F1,…,FT, an initial probability measure μ0 on E0, and transition and observation probability kernels Kt:Et−1→Et and Gt:Et→Ft for 1≤t≤T, as in General finite-horizon state-space models by kernels. On the interleaved finite product space ZT=E0×E1×F1×⋯×ET×FT, there is a unique probability measure whose rectangle probabilities are the ordered iterated kernel integrals μ0(dx0)∏t=1TKt(xt−1,dxt)Gt(xt,dyt). The product notation means successive integration in the displayed order. For T=0, the assertion is the initial law μ0 on E0.

Facts & Assumptions

Given: Assume AC and the finite-horizon spaces, initial law, and kernels in the Statement.

[A1]

AC asserts that every family of nonempty sets has a choice function. (The Axiom of Choice)

[F1]

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)

[F2]

A probability kernel has probability-measure sections and measurable evaluations. (Measure kernel and probability kernel)

[F3]

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)

[F4]

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)

[F5]

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.

1.1F1F2F5givenconstruct

If T=0, then Z0=E0 and μ0 is the unique asserted law. For T≥1, interleave the coordinate spaces as D0=E0, D2t−1=Et, and D2t=Ft for 1≤t≤T. At the transition to Et, define the lifted kernel by L2t−2(h,A)=Kt(πt−1(h),A), where πt−1 selects the Et−1 coordinate of the preceding history h; at the next transition define L2t−1(h,B)=Gt(πt(h),B), where πt selects the newly adjoined Et 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 D2T, append singleton spaces with their unit point-mass kernels.

2.1A1F3F4step 1.1

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 2T+1 coordinates is a probability measure on ZT; the prescribed prefix formula is exactly the displayed iterated integral. The finite product is standard Borel by [F4].

3.1A1F3F5step 2.1∎

Suppose QT is another probability measure on ZT with the same displayed rectangle probabilities. Append the singleton tail to the coordinate tuple by a measurable map and push QT 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 D2T 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 QT equals that marginal. For T=0, the formula already fixes Q0=μ0.

Depends on

Used by

Cited to discharge well-definedness by General finite-horizon state-space models by kernels.

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