feat: elapsed_between distribution accessor over a named chain (#274)#295
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feat: elapsed_between distribution accessor over a named chain (#274)#295seabbs-bot wants to merge 1 commit into
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Add elapsed_between(chain, from, to) and the origin-to-`to` form elapsed_between(chain, to): the LAW of the elapsed distance between two named events of a Sequential chain, as an ordinary univariate distribution (the convolution of the steps strictly between the two events). This is the distribution-level counterpart to the sample-level difference of two event positions, and deliberately NOT difference: two events on one chain descend from a shared origin, so difference(chain, chain) would double-count the shared leading steps. New accessor in src/composers/event_intervals.jl, built on the public convolved verb and the chain's event_names/observed leaves; it does not touch the convolution-layer internals. The event -> leaf-prefix mapping mirrors (not calls) _event_prefix_delay so the accessor does not couple to convolved_interop.jl. A branching chain, an out-of-order or unknown event pair, an uncertain-leaf chain, or a non-chain operand is rejected with a clear error. Tests use continuous components only. Co-authored-by: Sam Abbott <contact@samabbott.co.uk>
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Try this Pull Request!Option 1: Julia Package ManagerOpen Julia and type: import Pkg
Pkg.activate(temp=true)
Pkg.add(url="https://github.com/EpiAware/ComposedDistributions.jl", rev="feat/cd-274-elapsed-between")
using ComposedDistributionsOption 2: Local CheckoutIf you have the repo locally: git checkout feat/cd-274-elapsed-between
julia --project=. -e "using Pkg; Pkg.instantiate()" |
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📖 Documentation preview is ready! View the docs for this PR at: https://EpiAware.github.io/ComposedDistributions.jl/previews/PR295/ This preview will be updated automatically when you push new commits. |
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Benchmark comparison vs baseMinimum time per call. Buckets are PR time as a % of base, so lower is faster (🟢 faster, ⚪ within 5%, 🔴 slower). Counts of benchmarks per bucket:
Evaluation — 17 benchmarks (by time change)
AD gradients — 28 benchmarks (by time change)
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Closes #274.
What
elapsed_between(chain, from, to)returns the LAW of the elapsed distance between two named events of aSequentialchain, as an ordinary univariate distribution — the convolution of the steps strictly between the two events.elapsed_between(chain, to)is the origin-to-toform (the convolution of the chain prefix up toto).This is the distribution-level counterpart to the sample-level difference of two event positions, and deliberately not
difference: two events on one chain descend from a shared origin, so their absolute positions are not independent anddifference(chain, chain)would double-count the shared leading steps.elapsed_betweenconvolves only the intervening steps.Where / fences
src/composers/event_intervals.jl, built on the publicconvolvedverb and the chain'sevent_names/observed leaves. It does not editconvolved_interop.jlor anythingdiscretise_pmf-adjacent.convolved's value support — this PR encodes no assumption about it.convolved_interop.jl's_event_prefix_delayrather than calling it, so the accessor does not couple to the gated file. If the two later diverge, that is convolution-layer migration cleanup (Convolved#85).Errors (all clear ArgumentErrors)
Branching chain (events don't line up with delay steps), out-of-order or equal
from/to, an unknown event name, an uncertain-leaf chain (pin first), and a non-chain operand (Parallel/Choose/bare distribution) each error with a message naming the way forward. The cross-branch/data-selected cases the issue flags are covered by theParallel/Chooseguards.Verification
Two new testitems (
test/composers/event_intervals.jl), continuous components only. Full non-AD suite green (1554/1554).This was opened by a bot. Please ping @seabbs for any questions.