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Quantitative Finance · Glossaire

Qu'est-ce que « Indicator decomposition » ?

Definition 11.1 The Interview Book · Chapitre 11 — Probability II

The indicator decomposition of a count NN writes it as a sum of indicators, N=∑i1AiN = \sum_i \mathbf 1_{A_i}, so that E[N]=∑iP(Ai)\E[N] = \sum_i \P(A_i) by linearity, whether or not the events AiA_i are independent.

Exemples

Example 11.2 (Fixed points, runs, records)

A random permutation of nn items has on average one fixed point: item ii is in place with probability 1/n1/n, and there are nn items. A sequence of nn fair coin tosses has on average 1+(n−1)/21 + (n-1)/2 runs: a new run starts at toss i≥2i \ge 2 with probability 12\tfrac12. A sequence of nn independent draws from a continuous law has on average Hn=1+12+⋯+1nH_n = 1 + \tfrac12 + \dots + \tfrac1n records, since draw ii is the largest so far with probability 1/i1/i; for ten draws, about 2.93.

Example 11.3 (Distinct values)

nn draws from NN equally likely values show on average N(1−(1−1/N)n)N\big(1 - (1 - 1/N)^n\big) distinct values: value vv is missed by all draws with probability (1−1/N)n(1-1/N)^n. The same indicator gives the expected number of empty buckets in a hash table.

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