Adopt the setting and notation of The Synthetic Copy: Independent Cell Structure, Deterministic-Count Clocks, the Copy Measure, and the Smoothed Joint Density of Parameter and Observation Record , in particular N N N , B ~ \tilde{B} B ~ , η \eta η , the Poisson mass functions p o i μ \mathrm{poi}_\mu poi μ , and the σ \sigma σ -finite reference measure ρ \rho ρ : the probability space ( Ω , F , P ) (\Omega,\mathcal{F},P) ( Ω , F , P ) with the driving variables U i c , j U^{c,j}_i U i c , j , the σ \sigma σ -algebras U \mathcal{U} U and V \mathcal{V} V , the event Ω 0 U \Omega^{U}_0 Ω 0 U , the cells I c , j I_{c,j} I c , j with lengths μ c , j = ∣ I c , j ∣ > 0 \mu_{c,j}=|I_{c,j}|>0 μ c , j = ∣ I c , j ∣ > 0 indexed by the finite set L \mathsf{L} L with d d d elements, the cell-count vector K \mathsf{K} K , the deterministic-count clocks P ( y ) \mathsf{P}^{(y)} P ( y ) and the copy clocks P ♯ \mathsf{P}^{\sharp} P ♯ (with P ♯ ( ω ) = P ( K ( ω ) ) ( ω ) \mathsf{P}^{\sharp}(\omega)=\mathsf{P}^{(\mathsf{K}(\omega))}(\omega) P ♯ ( ω ) = P ( K ( ω )) ( ω ) ), the likelihoods ℓ ( y ) , ω \ell^{(y),\omega} ℓ ( y ) , ω and ℓ ♯ , ω = ℓ ( K ( ω ) ) , ω \ell^{\sharp,\omega}=\ell^{(\mathsf{K}(\omega)),\omega} ℓ ♯ , ω = ℓ ( K ( ω )) , ω , the observation record space ( R , R , ρ ) (\mathbf{R},\mathcal{R},\rho) ( R , R , ρ ) with R ( k ) \mathbf{R}^{(k)} R ( k ) the records with k k k events, the parameter lattice S = { y / N : y ∈ N 0 L } \mathsf{S}=\{y/\sqrt{N}:y\in\mathbb{N}_0^{\mathsf{L}}\} S = { y / N : y ∈ N 0 L } , the count mass function p \mathsf{p} p , the record kernel f f f , and the standard basis vectors e c , j e_{c,j} e c , j of R d \mathbb{R}^d R d ; E \mathbb{E} E is the expectation on ( Ω , F , P ) (\Omega,\mathcal{F},P) ( Ω , F , P ) , extended to [ 0 , ∞ ] [0,\infty] [ 0 , ∞ ] -valued measurable maps, 1 { ⋅ } \mathbf{1}\{\cdot\} 1 { ⋅ } the indicator of an event, and N 0 = { 0 } ∪ N \mathbb{N}_0=\{0\}\cup\mathbb{N} N 0 = { 0 } ∪ N with N \mathbb{N} N the natural numbers . Fix a natural number m ≥ 1 \mathsf{m}\ge1 m ≥ 1 (the move size ) and weights w = ( w c , j ) ( c , j ) ∈ L ∈ R d w=(w_{c,j})_{(c,j)\in\mathsf{L}}\in\mathbb{R}^d w = ( w c , j ) ( c , j ) ∈ L ∈ R d , and consider the d d d moves a c , j = m e c , j / N a_{c,j}=\mathsf{m}e_{c,j}/\sqrt{N} a c , j = m e c , j / N (( c , j ) ∈ L (c,j)\in\mathsf{L} ( c , j ) ∈ L ), indexed by L \mathsf{L} L through its fixed bijection with { 1 , … , d } \{1,\dots,d\} { 1 , … , d } ; by claim 5 of The Synthetic Copy: Independent Cell Structure, Deterministic-Count Clocks, the Copy Measure, and the Smoothed Joint Density of Parameter and Observation Record , x + a c , j ∈ S x+a_{c,j}\in\mathsf{S} x + a c , j ∈ S for all x ∈ S x\in\mathsf{S} x ∈ S , and Gaussian Smoothing of a Kernel-Weighted Discrete Family with Countable Support: Joint Density, van Trees Regularity, and the Symmetrised Directional Score Bound applies to ( d , η , p , f ) (d,\eta,\mathsf{p},f) ( d , η , p , f ) on ( R , R , ρ ) (\mathbf{R},\mathcal{R},\rho) ( R , R , ρ ) with n = d n=d n = d moves a 1 , … , a d a_1,\dots,a_d a 1 , … , a d and weights w 1 , … , w d w_1,\dots,w_d w 1 , … , w d , the move a q a_q a q being a c , j a_{c,j} a c , j and the weight w q w_q w q being w c , j w_{c,j} w c , j for q q q the image of ( c , j ) (c,j) ( c , j ) under the fixed bijection; let p c , j \mathsf{p}_{c,j} p c , j , f c , j f_{c,j} f c , j be its shifted family and J s y m ∈ [ 0 , ∞ ] \mathsf{J}^{\mathrm{sym}}\in[0,\infty] J sym ∈ [ 0 , ∞ ] its symmetrised kernel-weighted move information, and let Φ w : [ 0 , ∞ ) 1 + d → [ 0 , ∞ ) \Phi_w:[0,\infty)^{1+d}\to[0,\infty) Φ w : [ 0 , ∞ ) 1 + d → [ 0 , ∞ ) be the symmetrised score functional for the weights w w w (its last d d d arguments indexed by L \mathsf{L} L ).
For ( c , j ) ∈ L (c,j)\in\mathsf{L} ( c , j ) ∈ L let ϱ c , j = ϱ μ c , j m : N 0 → [ 0 , ∞ ) \varrho_{c,j}=\varrho^{\mathsf{m}}_{\mu_{c,j}}:\mathbb{N}_0\to[0,\infty) ϱ c , j = ϱ μ c , j m : N 0 → [ 0 , ∞ ) be the removal ratio with parameter μ c , j \mu_{c,j} μ c , j and move size m \mathsf{m} m , so that ϱ c , j ( k ) = p o i μ c , j ( k − m ) / p o i μ c , j ( k ) \varrho_{c,j}(k)=\mathrm{poi}_{\mu_{c,j}}(k-\mathsf{m})/\mathrm{poi}_{\mu_{c,j}}(k) ϱ c , j ( k ) = poi μ c , j ( k − m ) / poi μ c , j ( k ) for k ≥ m k\ge\mathsf{m} k ≥ m and ϱ c , j ( k ) = 0 \varrho_{c,j}(k)=0 ϱ c , j ( k ) = 0 for k < m k<\mathsf{m} k < m (the symbol ϱ \varrho ϱ is distinct from the reference measure ρ \rho ρ , and the cell lengths μ c , j \mu_{c,j} μ c , j from the copy measure μ ♯ \mu^{\sharp} μ ♯ ). The removed-clock likelihood for the cell ( c , j ) (c,j) ( c , j ) is the map ( r , ω ) ↦ ℓ − ( c , j ) , ω ( r ) (r,\omega)\mapsto\ell^{-(c,j),\omega}(r) ( r , ω ) ↦ ℓ − ( c , j ) , ω ( r ) on R × Ω \mathbf{R}\times\Omega R × Ω given by
ℓ − ( c , j ) , ω = ℓ ( K ( ω ) − m e c , j ) , ω if K c , j ( ω ) ≥ m , ℓ − ( c , j ) , ω = 0 otherwise . \ell^{-(c,j),\omega}=\ell^{(\mathsf{K}(\omega)-\mathsf{m}e_{c,j}),\omega}\ \text{ if }\mathsf{K}_{c,j}(\omega)\ge\mathsf{m},\qquad \ell^{-(c,j),\omega}=0\ \text{ otherwise}. ℓ − ( c , j ) , ω = ℓ ( K ( ω ) − m e c , j ) , ω if K c , j ( ω ) ≥ m , ℓ − ( c , j ) , ω = 0 otherwise .
1. (Removed clocks) Let ( c , j ) ∈ L (c,j)\in\mathsf{L} ( c , j ) ∈ L and ω ∈ Ω \omega\in\Omega ω ∈ Ω with K c , j ( ω ) ≥ m \mathsf{K}_{c,j}(\omega)\ge\mathsf{m} K c , j ( ω ) ≥ m , and put y = K ( ω ) − m e c , j ∈ N 0 L y=\mathsf{K}(\omega)-\mathsf{m}e_{c,j}\in\mathbb{N}_0^{\mathsf{L}} y = K ( ω ) − m e c , j ∈ N 0 L . Then P u ♯ , c ′ ( ω ) = P u ( y ) , c ′ ( ω ) \mathsf{P}^{\sharp,c'}_u(\omega)=\mathsf{P}^{(y),c'}_u(\omega) P u ♯ , c ′ ( ω ) = P u ( y ) , c ′ ( ω ) for every label c ′ ≠ c c'\neq c c ′ = c and every u ≥ 0 u\ge0 u ≥ 0 , and
P u ♯ , c ( ω ) = P u ( y ) , c ( ω ) + 1 Ω 0 U ( ω ) ∑ i = K c , j ( ω ) − m + 1 K c , j ( ω ) 1 { U i c , j ( ω ) ≤ u } ( u ≥ 0 ) : \mathsf{P}^{\sharp,c}_u(\omega)=\mathsf{P}^{(y),c}_u(\omega)+\mathbf{1}_{\Omega^{U}_0}(\omega)\sum_{i=\mathsf{K}_{c,j}(\omega)-\mathsf{m}+1}^{\mathsf{K}_{c,j}(\omega)}\mathbf{1}\{U^{c,j}_i(\omega)\le u\}\qquad(u\ge0): P u ♯ , c ( ω ) = P u ( y ) , c ( ω ) + 1 Ω 0 U ( ω ) ∑ i = K c , j ( ω ) − m + 1 K c , j ( ω ) 1 { U i c , j ( ω ) ≤ u } ( u ≥ 0 ) :
the copy clocks are the clocks P ( y ) ( ω ) \mathsf{P}^{(y)}(\omega) P ( y ) ( ω ) with the m \mathsf{m} m points U i c , j ( ω ) U^{c,j}_i(\omega) U i c , j ( ω ) , K c , j ( ω ) − m < i ≤ K c , j ( ω ) \mathsf{K}_{c,j}(\omega)-\mathsf{m}<i\le\mathsf{K}_{c,j}(\omega) K c , j ( ω ) − m < i ≤ K c , j ( ω ) , inserted into the cell I c , j I_{c,j} I c , j of the clock c c c ; on Ω 0 U \Omega^{U}_0 Ω 0 U these points lie in I c , j I_{c,j} I c , j , are pairwise distinct, and differ from every point U i ′ c , j ′ ( ω ) U^{c,j'}_{i'}(\omega) U i ′ c , j ′ ( ω ) with 1 ≤ j ′ ≤ J c 1\le j'\le J_c 1 ≤ j ′ ≤ J c and 1 ≤ i ′ ≤ y c , j ′ 1\le i'\le y_{c,j'} 1 ≤ i ′ ≤ y c , j ′ . The likelihood ℓ − ( c , j ) , ω = ℓ ( y ) , ω \ell^{-(c,j),\omega}=\ell^{(y),\omega} ℓ − ( c , j ) , ω = ℓ ( y ) , ω is the record-driven likelihood of The Record-Driven Causal Intensity of the Open-Loop Aggregate Solution: Joint Measurability of the Record-Frozen Control, the Regularised Recursion Path, Non-Anticipation, and Measurability of the Likelihood for the clock family P ( y ) ( ω ) \mathsf{P}^{(y)}(\omega) P ( y ) ( ω ) .
2. (Measurability and moments) For every ( c , j ) ∈ L (c,j)\in\mathsf{L} ( c , j ) ∈ L : the map ( r , ω ) ↦ ℓ − ( c , j ) , ω ( r ) (r,\omega)\mapsto\ell^{-(c,j),\omega}(r) ( r , ω ) ↦ ℓ − ( c , j ) , ω ( r ) is measurable with respect to the product σ \sigma σ -algebra R ⊗ F \mathcal{R}\otimes\mathcal{F} R ⊗ F and takes values in [ 0 , ( N B ~ ) k ] [0,(N\tilde{B})^{k}] [ 0 , ( N B ~ ) k ] on R ( k ) × Ω \mathbf{R}^{(k)}\times\Omega R ( k ) × Ω (with ( N B ~ ) 0 = 1 (N\tilde{B})^{0}=1 ( N B ~ ) 0 = 1 ); ϱ c , j ( K c , j ) \varrho_{c,j}(\mathsf{K}_{c,j}) ϱ c , j ( K c , j ) is a V \mathcal{V} V -measurable random variable with
E [ ϱ c , j ( K c , j ) ] = 1 , E [ ϱ c , j ( K c , j ) 2 ] = ∑ i = 0 m ( m i ) 2 i ! μ c , j − i ; \mathbb{E}\bigl[\varrho_{c,j}(\mathsf{K}_{c,j})\bigr]=1,\qquad \mathbb{E}\bigl[\varrho_{c,j}(\mathsf{K}_{c,j})^{2}\bigr]=\sum_{i=0}^{\mathsf{m}}\binom{\mathsf{m}}{i}^{2}\,i!\,\mu_{c,j}^{-i}; E [ ϱ c , j ( K c , j ) ] = 1 , E [ ϱ c , j ( K c , j ) 2 ] = ∑ i = 0 m ( i m ) 2 i ! μ c , j − i ;
and the pathwise score map
Ψ ( r , ω ) = Φ w ( ℓ ♯ , ω ( r ) , ( ϱ c , j ( K c , j ( ω ) ) ℓ − ( c , j ) , ω ( r ) ) ( c , j ) ∈ L ) ( ( r , ω ) ∈ R × Ω ) \Psi(r,\omega)=\Phi_w\Bigl(\ell^{\sharp,\omega}(r),\ \bigl(\varrho_{c,j}(\mathsf{K}_{c,j}(\omega))\,\ell^{-(c,j),\omega}(r)\bigr)_{(c,j)\in\mathsf{L}}\Bigr)\qquad((r,\omega)\in\mathbf{R}\times\Omega) Ψ ( r , ω ) = Φ w ( ℓ ♯ , ω ( r ) , ( ϱ c , j ( K c , j ( ω )) ℓ − ( c , j ) , ω ( r ) ) ( c , j ) ∈ L ) (( r , ω ) ∈ R × Ω )
is R ⊗ F \mathcal{R}\otimes\mathcal{F} R ⊗ F -measurable with values in [ 0 , ∞ ) [0,\infty) [ 0 , ∞ ) ; consequently r ↦ E [ Ψ ( r , ⋅ ) ] r\mapsto\mathbb{E}[\Psi(r,\cdot)] r ↦ E [ Ψ ( r , ⋅ )] is R \mathcal{R} R -measurable and ω ↦ ∫ R Ψ ( r , ω ) ρ ( d r ) \omega\mapsto\int_{\mathbf{R}}\Psi(r,\omega)\,\rho(dr) ω ↦ ∫ R Ψ ( r , ω ) ρ ( d r ) is F \mathcal{F} F -measurable, both with values in [ 0 , ∞ ] [0,\infty] [ 0 , ∞ ] (by the Tonelli theorem , ρ \rho ρ being σ \sigma σ -finite and P P P finite).
3. (The shifted family on the copy) For every x = y / N ∈ S x=y/\sqrt{N}\in\mathsf{S} x = y / N ∈ S , every r ∈ R r\in\mathbf{R} r ∈ R and every ( c , j ) ∈ L (c,j)\in\mathsf{L} ( c , j ) ∈ L ,
p ( x ) f ( x , r ) = E [ 1 { K = y } ℓ ♯ , ⋅ ( r ) ] , p c , j ( x ) f c , j ( x , r ) = E [ 1 { K = y } ϱ c , j ( K c , j ) ℓ − ( c , j ) , ⋅ ( r ) ] . \mathsf{p}(x)f(x,r)=\mathbb{E}\bigl[\mathbf{1}\{\mathsf{K}=y\}\,\ell^{\sharp,\cdot}(r)\bigr],\qquad \mathsf{p}_{c,j}(x)f_{c,j}(x,r)=\mathbb{E}\bigl[\mathbf{1}\{\mathsf{K}=y\}\,\varrho_{c,j}(\mathsf{K}_{c,j})\,\ell^{-(c,j),\cdot}(r)\bigr]. p ( x ) f ( x , r ) = E [ 1 { K = y } ℓ ♯ , ⋅ ( r ) ] , p c , j ( x ) f c , j ( x , r ) = E [ 1 { K = y } ϱ c , j ( K c , j ) ℓ − ( c , j ) , ⋅ ( r ) ] .
4. (Pathwise reduction) In [ 0 , ∞ ] [0,\infty] [ 0 , ∞ ] ,
J s y m ≤ E [ ∫ R Ψ ( r , ⋅ ) ρ ( d r ) ] = ∫ R E [ Ψ ( r , ⋅ ) ] ρ ( d r ) . \mathsf{J}^{\mathrm{sym}}\ \le\ \mathbb{E}\Bigl[\int_{\mathbf{R}}\Psi(r,\cdot)\,\rho(dr)\Bigr]=\int_{\mathbf{R}}\mathbb{E}\bigl[\Psi(r,\cdot)\bigr]\,\rho(dr). J sym ≤ E [ ∫ R Ψ ( r , ⋅ ) ρ ( d r ) ] = ∫ R E [ Ψ ( r , ⋅ ) ] ρ ( d r ) .