(f) For each of the following families of distributions, explain whet her it is an exponential family: Bernoulli(p), pe (0, 1) N (u, 1), ueR N (u, a), ne R and o >0 Exponential(A), >0 Uniform((0, 0)), e>0 • r(a, 3), where a, 3>0 Poisson(A), A> 0 • Binomial with parametersn and p • Binomial with n= 100 and other parameter being p Cauchy distribution centered around 0.

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please tell all parts of question f

(f)
For each of the following families of distributions, explain whether it is an exponential family:
Bernoulli(p), p E (0, 1)
Ν (μ, 1), μeR
Ν (μ, σ') , με R md σ? >0
Exponential(A), 1> 0
Uniform([0, O]), 0 >0
• r(a, 3), where a, 3 > 0
• Poisson(A), > 0
• Binomial with parameters n and p
• Binomial with n = 100 and other parameter being p
Cauchy distribution centered around 0.
Transcribed Image Text:(f) For each of the following families of distributions, explain whether it is an exponential family: Bernoulli(p), p E (0, 1) Ν (μ, 1), μeR Ν (μ, σ') , με R md σ? >0 Exponential(A), 1> 0 Uniform([0, O]), 0 >0 • r(a, 3), where a, 3 > 0 • Poisson(A), > 0 • Binomial with parameters n and p • Binomial with n = 100 and other parameter being p Cauchy distribution centered around 0.
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A k-parameter exponential family {f(x;θ):θΩRk} of distributions that can be expressed as f(x;θ)=exp[i=1kui(θ)Ti(x)+ν(θ)+w(x)] with the following regularity conditions:

1. The support (x:f(x;θ)>0) does not depend on 2. The parameter space Ω is an open subset in Rk, containing k dimensional rectangle3. {1, T1(x), T2(x), ...., Tk(x)} and {1, u1(θ), u2(θ),...., uk(θ)} are linearly independent

; is defined as k- parameter exponential family.

a.)

X~Bernoulli(p)f(x;p)= px(1-p)1-x=p1-px(1-p)=exp[xlnp1-p](1-p)Here, k=1,u1(p)=lnp1-pT1(x)=xv(p)=ln(1-p)Thus, X is a one-parameter exponential family

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