Proof of The Copy Clocks Are Independent Poisson Clocks with Horizon R, and Every Record Is Almost Surely Conflict-Free for Them
lemmalem:copy-clocks-independent-poisson-horizon-2026aThroughout, ranges over the transition labels, a finite nonempty set. Recall that is the event that, for every label , the points (, ) are pairwise distinct and ; it is an event with by claim 1 of The Synthetic Copy: Independent Cell Structure, Deterministic-Count Clocks, the Copy Measure, and the Smoothed Joint Density of Parameter and Observation Record (it contains the intersection over of the probability-one events of claim 1 of Uniform Representation of the Rate-One Poisson Counting Path on a Bounded Interval: Distinct Points, Poisson Increments, Conditional Law Given the Cell Counts, and Point Insertion attached to the -th block of driving variables). Each is a random variable: is a pointwise limit of finite sums of products of indicators of events (Arithmetic, Absolute Values, and Pointwise Limits of Measurable Real-Valued Functions).
Claim 1. Fix and . Off the path is identically , a counting path. On the path is , a finite sum of indicators of half-lines with pairwise distinct left endpoints in : it vanishes at , takes values in , is nondecreasing and right-continuous (each indicator is), and has unit jumps because the endpoints are distinct; so it is a counting path (Counting Path and Its Jump Times). For every indicator equals , so the path is constant on .
Claim 2. is a stochastic process by the measurability just noted, and property 1 of Poisson Clock with a Horizon is claim 1. For the remaining properties, let be real numbers and write (). By claim 1, identically if , and if ; in all cases . Let be the number of indices with . The increments are the increments of over the partition of a subinterval of (the group being empty when ), hence are independent, having the Poisson distribution with parameter , by claim 2 of The Copy Clocks Have Independent Poisson Increments on the Clock Interval: Law Identity with the Uniform Poisson Path and Applicability of the Window Discrepancy Bound; and are identically , which has the Poisson distribution with parameter (the unit mass at , Poisson Distribution). A random variable that is identically is independent of every family of random variables: for a Borel set the event is or , so every joint probability factorizes (Independence of Events and of Random Variables). Hence are independent: this is property 2 of Poisson Clock with a Horizon, and the increment laws are property 3.
Claim 3. For each label let be the -algebra generated by the -th block of driving variables, , () and (, ). Since the whole family of driving variables is independent and the blocks are disjoint, the family is independent by the grouping lemma (applied after enumerating the countable index set of the driving variables, independence being unaffected by reindexing). Put (); as is the cell count of Uniform Representation of the Rate-One Poisson Counting Path on a Bounded Interval: Distinct Points, Poisson Increments, Conditional Law Given the Cell Counts, and Point Insertion for the -th block, hence measurable with respect to the -algebra generated by and the and a fortiori -measurable, each is -measurable (Arithmetic, Absolute Values, and Pointwise Limits of Measurable Real-Valued Functions: the sum equals , a pointwise limit of finite sums of products of -measurable indicators), and on . Let be the class of events for which there is an event with . This class is a -algebra (it contains , and complements and countable unions of the 's serve for complements and countable unions of the 's), and it contains the generators (, a Borel set), with ; hence every admits such an , and since . Now let be given for the labels in a finite set of labels, with as above. Then and agree on , so
the middle equality by the independence of . This is the independence of the family , ranging over the labels (Sigma-Algebra Generated by Random Variables and Independence of Sigma-Algebras).
Claim 4. Fix . For , on the event one has for all and , by the definition ; hence the data and coincide, and if and only if . Therefore
a countable union ( is countable, Products and Powers of Countable Sets) of events ( is an event since is a random vector with values in , and is an event by claim 2 of Almost Sure Tracking on the Synthetic Copy: Jump Times of the Deterministic-Count Clocks, Almost Sure Conflict-Freeness of Every Record, Almost Sure Null Mass of the Untracked Records, and Trimming an Event to the Tracked Set), so is an event; each member of the union has probability zero, since by the same claim 2. By countable subadditivity (claim 4 of Basic Properties of a Measure), .
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Prerequisites
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