Burn-in — where it appears
Named by 7 essays across 3 fields — each of them below, with the objects they name alongside it.
A test rather than a survey
A thin admissible set falls into an arrangement and its mirror image, and the walk that samples it is uniform on half the reference distribution for ever. That was found by enumerating fourteen units, and enumeration stops at twenty-four.
The statistic that changes sign
A test for an unreachable half needs a quantity that tells one half from the other. Every symmetric reading of a mirror pair is identical, and a magnitude is the natural thing to reach for.
The set a dictionary leaves
A rule constrained on six functions at a loose tolerance leaves a set as thin as one constrained on three at a tight one. Both sampling methods cross over at the same thinness, and the tolerance where that happens moves by a factor of three.
Walking the admissible set
A rerandomisation test hunts for admissible assignments and throws away the rest. A walk visits them instead — and it is exactly uniform only because it stands still when a proposal fails, which is the step that looks like waste.
Draws that repeat each other
A hunt costs 1/p evaluations per independent draw. A walk costs one per step and yields an effective draw every τ steps. Both are counted in the same unit, and the walk is dearer at every tolerance a trial is designed at.
The diagnostic at two hundred
Pointed at a trial size no enumeration reaches, the test gives three answers rather than one — and past a certain thinness it stops agreeing with itself, which is the honest reading and the one nothing could give before.
The walk that cannot cross
A thin enough admissible set is not one set. It splits into an assignment and its mirror image, no sequence of admissible single swaps joins them, and the walk that samples it is uniform on half the reference distribution for ever.
Named alongside it
The objects these essays reach for when they reach for this one.
Markov chain Monte CarloRandomisation testReference distributionCovariate balanceRerandomisationDetailed balanceEffective sample sizeAssignment mechanismCombinatorial searchCritical valueImbalanceAcceptance rate