Reason: First version: score identities and the mixture-weight directional van Trees inequality (P4.4), replacing the plain van Trees information by the symmetrised one.
(iv′) Square-integrable scores. For each i∈{1,…,l} the score Si=∂ip(Θ,D)/p(Θ,D), a random variable exactly as noted in hypothesis (iv) there, is square-integrable.
Write E for the expectation, ∥U∥2 for the mean-square norm, x⋅y for the dot product and x−y for the difference on the Euclidean spaceRl, ∣⋅∣ for the absolute value, and t for the nonnegative square root of a real t≥0. For u=(u1,…,ul)∈Rl put ∂up=∑i=1lui∂ip and Su=∑i=1luiSi, and for α∈Rl put α⋅Θ=∑j=1lαjΘj; sums over finite index sets are the finite sums of the real numbers. Throughout, m:Y→R denotes a map measurable with respect to G and the Borel σ-algebra such that m(D)=m∘D is square-integrable.
1. (Score identities) For every such m and all i,j∈{1,…,l},
E[m(D)Si]=0,E[ΘjSi]=−δij,
where δij=1 if i=j and δij=0 otherwise. Consequently, for all u,α∈Rl,
E[(m(D)−α⋅Θ)Su]=α⋅u.
2. (Mixture-weight directional bound) Let n≥1 be a natural number, let a1,…,an∈Rl, and define the mixture weightpˉ:Rl×Y→R by
pˉ(θ,y)=21p(θ,y)+2n1j=1∑np(θ−aj,y).
Then pˉ is strictly positive and measurable with respect to Bl⊗G and the Borel σ-algebra, ∫pˉdκ=1, and for every u∈Rl the function (∂up)2/pˉ is measurable with
Iu=∫Rl×Ypˉ(∂up)2dκ≤2E[Su2]<∞,
called the mixture-weight information in the direction u for the shifts a1,…,an. Moreover, for every m as above and every α∈Rl,
Curated associations between results. These are editable and subjective — they do not replace the dependency graph, which is derived from the references in the text.