The top documents tagged [indicator random variable]

Indicator Random Variables

©2001 by Charles E. Leiserson Introduction to AlgorithmsDay 9 L6.1 Introduction to Algorithms 6.046J/18.401J/SMA5503 Lecture 6 Prof. Erik Demaine.

October 17, 2005 Copyright©2001-5 Erik D. Demaine and Charles E. Leiserson L2.1 Introduction to Algorithms 6.046J/18.401J LECTURE9 Randomly built binary.

Probabilistic (Average-Case) Analysis and Randomized Algorithms Two different approaches –Probabilistic analysis of a deterministic algorithm –Randomized.

T i = indicator random variable of the event that i-th throw results in a tail E[T] = E[T 1 ] + … + E[T 6 ] = 6*(1/2) = 3 P(T=3) = P(H=3) = binomial(6,3)/2.

–Def: A language L is in BPP c,s ( 0 s(n) c(n) 1, n N) if there exists a probabilistic poly-time TM M s.t. : 1. w L, Pr[M accepts w] c(|w|),

8.Sorting in linear time Hsu, Lih-Hsing. Computer Theory Lab. Chapter 8P.2 8.1 Lower bound for sorting The decision tree model.

T i = indicator random variable of the event that i-th throw results in a tail

Probabilistic (Average-Case) Analysis and Randomized Algorithms

X i = indicator random variable of the event that i-th person gets his hat back. E[X i ]=1/20 X=X 1 +…+X 20 E[X] = E[X 1 ] +…+ E[X 20 ] = 1.

X i = indicator random variable of the event that i-th person gets his hat back.