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Probabiliity and Mathematical Statistics

Balls into Bins

2019-09-04Original-language archivelegacy assets may be incomplete

Balls into Bins

mm balls into nn bins uniformly and independently

Birthday Problem

the mapping is 1-1

  • pigeonhole principle: P=1,m>365P=1,m>365
  • P(one-to-one)=k=1m1(1kn)em22nP(\text{one-to-one})=\prod_{k=1}^{m-1}(1-\frac{k}{n})\approx e^{-\frac{m^2}{2n}}
  • P(collision)=1P=1ϵ,m=2nln1ϵP(\text{collision})=1-P=1-\epsilon,m=\sqrt{2n\ln\frac{1}{\epsilon}}
    • P>0.99,m>57P>0.99,m>57

Coupon Collector Problem

the mapping is on-to

  • XX is the number of balls thrown uniformly and independtly to nn bins until no bin is empty
  • E(X)=nH(n)=ni=1n1i\mathbb{E}(X)=nH(n)=n\sum_{i=1}^n\frac{1}{i}
  • P(Xnlnn+cn)<ec,c>0P(X\geq n\ln n+cn)<e^{-c},\forall c>0
  • limnP(Xnlnn+cn)=1eec\lim_{n\rightarrow\infty}P(X\geq n\ln n+cn)=1-e^{-e^{-c}}

Occupancy Problem

the maximum load of bins

  • XiX_i: load of iith bin
  • Average: E(Xi)=mn\mathbb{E}(X_i)=\frac{m}{n}

maxiXi={O(lognloglogn)m=Θ(n)w.h.pO(mn)m=Ω(nlogn)w.h.p\max_i X_i=\begin{cases}O(\frac{\log n}{\log\log n})&m=\Theta(n)&w.h.p\newline O(\frac{m}{n})&m=\Omega(n\log n)&w.h.p\newline \end{cases}

  • 2-choice: maxiXi=Θ(loglogn),m=Θ(n)\max_i X_i=\Theta(\log\log n),m=\Theta(n) w.h.p