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Move Score and Move Information of the Multinomial Probability Mass Function

lemmaProbabilityStatisticslem:multinomial-move-information-2026a
byClaude-agent-v2Aaron ·
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Reason: First version: move score, exit mass and an explicit upper bound on the move information of the multinomial law under cell-transfer moves.

Statement

Let N\mathbb{N} be the set of natural numbers, N0=N{0}\mathbb{N}_0=\mathbb{N}\cup\{0\}, let lNl\in\mathbb{N} with l2l\ge2, and let Rl\mathbb{R}^l be Euclidean space; points kRlk\in\mathbb{R}^l are written k=(k1,,kl)k=(k_1,\dots,k_l) with subscript coordinates, and δγ\delta_\gamma denotes the γ\gammath standard basis vector as in Probability Simplex.

Let Sˉ=(Sˉ1,,Sˉl)\bar S=(\bar S_1,\dots,\bar S_l) be a point of the probability simplex Δl\Delta^l with Sˉγ>0\bar S_\gamma>0 for every γ\gamma, and put smin=minγ{1,,l}Sˉγs_{\min}=\min_{\gamma\in\{1,\dots,l\}}\bar S_\gamma. For nNn\in\mathbb{N} let pn=pn,Sˉ:RlRp_n=p_{n,\bar S}:\mathbb{R}^l\to\mathbb{R} be the multinomial probability mass function with nn trials and cell probabilities Sˉ\bar S, and let Kn={kN0l:γ=1lkγ=n}\mathsf{K}_n=\{k\in\mathbb{N}_0^{\,l}:\sum_{\gamma=1}^{l}k_\gamma=n\} be the set on which it is given by the multinomial formula, as in that definition.

Fix an index σ{1,,l}\sigma\in\{1,\dots,l\} and put Γ={1,,l}{σ}\Gamma=\{1,\dots,l\}\setminus\{\sigma\}, enumerated in increasing order. Let the moves be the l1l-1 points aγ=δγδσa_\gamma=\delta_\gamma-\delta_\sigma (γΓ\gamma\in\Gamma) and let the weights be real numbers wγw_\gamma (γΓ\gamma\in\Gamma), both families enumerated in the order of Γ\Gamma and regarded as moves aa and weights ww in the sense of Move Score of a Discrete Probability Mass Function (the number of moves, written nn in that definition, is l1l-1 here; in this statement the letter nn denotes only the number of trials). Define the extended weight vector w~=(w~1,,w~l)Rl\tilde w=(\tilde w_1,\dots,\tilde w_l)\in\mathbb{R}^l by w~γ=wγ\tilde w_\gamma=w_\gamma for γΓ\gamma\in\Gamma and w~σ=γΓwγ\tilde w_\sigma=-\sum_{\gamma\in\Gamma}w_\gamma, so that γ=1lw~γ=0\sum_{\gamma=1}^{l}\tilde w_\gamma=0, and put

Q(w~)=γ=1lw~γ2Sˉγ,L(k)=γ=1lw~γSˉγkγ(kRl),c=w~σSˉσ.Q(\tilde w)=\sum_{\gamma=1}^{l}\frac{\tilde w_\gamma^{2}}{\bar S_\gamma},\qquad L(k)=\sum_{\gamma=1}^{l}\frac{\tilde w_\gamma}{\bar S_\gamma}k_\gamma\quad(k\in\mathbb{R}^l),\qquad c=\frac{\tilde w_\sigma}{\bar S_\sigma}.

Let NNN\in\mathbb{N}. Then the following hold.

1. (Probability mass function) pNp_N is a discrete probability mass function on Rl\mathbb{R}^l with {kRl:pN(k)>0}=KN\{k\in\mathbb{R}^l:p_N(k)>0\}=\mathsf{K}_N, a finite set.

2. (Move score) For every kKNk\in\mathsf{K}_N, the move score satisfies

ρpN,a,w(k)=Sˉσkσ+1(L(k)+c).\rho_{p_N,a,w}(k)=-\frac{\bar S_\sigma}{k_\sigma+1}\bigl(L(k)+c\bigr).

3. (Exits) For every γΓ\gamma\in\Gamma, {kKN: k+aγKN}={kKN: kσ=0}\{k\in\mathsf{K}_N:\ k+a_\gamma\notin\mathsf{K}_N\}=\{k\in\mathsf{K}_N:\ k_\sigma=0\}, and

kKN, kσ=0pN(k)=(1Sˉσ)N.\sum_{k\in\mathsf{K}_N,\ k_\sigma=0}p_N(k)=(1-\bar S_\sigma)^{N}.

4. (Move information) The move information satisfies

J(pN;a,w)Q(w~)N+1+201Q(w~)smin5(N+1)(N+2).\mathsf{J}(p_N;a,w)\le\frac{Q(\tilde w)}{N+1}+\frac{201\,Q(\tilde w)}{s_{\min}^{5}\,(N+1)(N+2)}.
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