For any discrete random variable \(X\), with probability mass function…

GATE · 2017 · CS · Set 2 · Computer Science & IT

For any discrete random variable XX, with probability mass function

P(X=j)=pj,pj≥0,j∈{0,…,N}P(X=j)=p_j, p_j \geq 0, j \in \{0, \dots , N \}, and Σj=0N pj=1\Sigma_{j=0}^N \: p_j =1, define the polynomial function gx(z)=Σj=0N pj zjg_x(z) = \Sigma_{j=0}^N \: p_j \: z^j. For a certain discrete random variable YY, there exists a scalar β∈[0,1]\beta \in [0,1] such that gy(z)=(1−β+βz)Ng_y(z) =(1- \beta+\beta z)^N. The expectation of YY is

  1. A.

    Nβ(1−β)N \beta(1-\beta)

  2. B.

    NβN \beta

  3. C.

    N(1−β)N (1-\beta)

  4. D.

    Not expressible in terms of NN and β\beta alone

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