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One-step weak defect of finite-state Poisson tau-leaping

Statement

Assume Countable Choice and Dependent Choice. Let E be finite, let A=max⁡({0}∪{∑jaj(x):x∈E}), and assume aj(x)=0 whenever x+νj∉E. Extend every state outside E as absorbing, as in Explicit Poisson tau-leaping with an invalid-state status. For h≥0, let Qh be the exact reaction-chain transition kernel and Kh the Poisson tau-leap transition kernel. If g:Zd→R is bounded, then ∣(Khg)(x)−g(x)−h(Qg)(x)∣≤2A2h2∥g∥∞, ∣(Qhg)(x)−g(x)−h(Qg)(x)∣≤2A2h2∥g∥∞, and hence ∥Khg−Qhg∥∞≤4A2h2∥g∥∞. The bounds include the multi-reaction leap probability that can trigger the invalid-state branch.

Facts & Assumptions

Given: Finite reaction data Finite-channel reaction propensity systems and their generator, the exact reaction chain law specified there, the tau-leap kernel Explicit Poisson tau-leaping with an invalid-state status, and a bounded g.

[F1]

Finite-channel reaction propensity systems and their generator: a single channel-j event from x has destination x+νj and rate aj(x); forbidden one-event transitions have zero propensity.

[F2]

Competing exponential clocks give the reaction event law: at state x, the next-event survival is e−λ(x)t and the channel-j event density is aj(x)e−λ(x)t.

[F3]

Explicit Poisson tau-leaping with an invalid-state status: conditional channel counts are independent Poisson variables of means aj(x)h and any out-of-E leap is absorbing.

[F4]

The scalar Poisson count law: the count mass is e−λλk/k! for finite λ.

[F5]

Normalization, moments, and generating function of the Poisson count law: a Poisson count of mean λ has mean λ and factorial second moment λ2.

[F6]

Finite-state Gillespie and next-reaction methods have the specified CTMC law: under Countable Choice and Dependent Choice, the finite-state reaction system has its specified exact path law and has finitely many events on finite horizons.

[A1]

Countable Choice is assumed by [F2] for the exponential probability law. The Axiom of Countable Choice (ACω)

Proof

Given: Countable Choice, Dependent Choice, a step h≥0, A as in the Statement, and G=∥g∥∞.

If E=∅, then A=0 and both kernels are the identity on Zd, so every asserted bound is zero. Otherwise fix x∈E.

1.1F1F3F4F5algebra

For the Poisson leap, the probability of zero total counts is e−λ(x)h. The probability that exactly channel j fires once is aj(x)he−λ(x)h, so its difference from aj(x)h is at most aj(x)λ(x)h2. Summing these errors over j gives at most λ(x)2h2≤A2h2. If N=∑jKj, independence and [F5] give E[N(N−1)∣x]=∑j(aj(x)h)2+2∑i<jai(x)aj(x)h2=(λ(x)h)2, hence P(N≥2∣x)≤(λ(x)h)2/2≤A2h2/2. The zero-count probability differs from 1−λ(x)h by at most A2h2/2. A single allowed channel event remains in E by [F1], so only the N≥2 part can trigger the invalid-state branch. The zero-count, total single-count, and multiple-count contributions are bounded respectively by GA2h2/2, GA2h2, and GA2h2/2. Thus ∣(Khg)(x)−g(x)−h(Qg)(x)∣≤2GA2h2.

1.2F1F2F6A1A2algebra

For the exact chain, the no-event probability is e−λ(x)h, which differs from 1−λ(x)h by at most A2h2/2. The probability of one channel-j event and no later event is ∫0haj(x)e−λ(x)se−λ(x+νj)(h−s) ds. Its difference from aj(x)h is at most aj(x)Ah2, since all total rates on E are at most A; summing over j gives at most λ(x)Ah2≤A2h2. The probability of two or more events is at most A2h2/2: conditional on a first event at time s, a later event has probability at most A(h−s), and integration against the first-event rate bounded by A gives the bound. The no-event, total single-event, and multiple-event contributions are bounded respectively by GA2h2/2, GA2h2, and GA2h2/2. Therefore ∣(Qhg)(x)−g(x)−h(Qg)(x)∣≤2GA2h2.

2.1step 1.1step 1.2algebra∎

Subtract the two first-order expansions from steps 1.1 and 1.2 and use the triangle inequality. This gives ∣(Khg)(x)−(Qhg)(x)∣≤4GA2h2 for every x∈E. Outside E both kernels hold the state fixed, so the same bound holds there. Taking the supremum proves the final inequality.

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