Let and . DefineThe dependency sum is over ordered pairs, so each unordered overlapping pair is counted twice. Disjoint prescribed edge sets give independence.
Order the events. For each , let be the event that all earlier events fail. Split it as , where excludes earlier events with edge sets disjoint from , and excludes the overlapping earlier events. These are down-sets. The event is a principal up-set, and it is independent of since their determining edge sets are disjoint.
If some has probability zero the desired zero-event bound is immediate. Otherwise the allowed conditional correlation inequality givesSince , it follows thatThe last step is the union bound under conditioning on . Zero-probability events may simply be omitted, and a certain event makes the target probability zero. Finally the sequential product and giveThis proves the first Janson inequality with the dependency-sum convention explicit.
Independently retain each event with probability , using a fresh Bernoulli mark for every index. Its retained occurrence is the original principal up-set together with its mark being one, so the first proof applies in the enlarged independent product space. The new parameters are and : distinct retained events have independent marks, and pairs of disjoint original supports remain independent.
If no original event occurs, no retained event occurs. ThusWhen , choose to minimize this quadratic exponent. It givesIf , the appropriate trivial bound is one, rather than an undefined zero-over-zero exponent. The argument is the Janson lower-tail bound by independent thinning.
Count one copy for each four-element vertex set. It requires six independent edges, soDistinct copies share an edge exactly when their vertex intersection has size two or three. For each first copy, their numbers are and , respectively. The required union then has eleven or nine edges. Hence the ordered Janson dependency sum isFor large , and , so and for some constant . The second Janson inequality therefore givesThis is the asserted asymptotic bound. It can also be made valid for every by decreasing to cover the finitely many smaller values, since each then has probability strictly below one of containing no copy. For the event has probability one and no positive exponential bound of this form can hold.
Articles by others on the same topic
There are currently no matching articles.