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using Distributions
using Optim

const N=1000
const K=10
genX = MvNormal(eye(K))
X = rand(genX,N)
X = X'
constant = ones(N)
X = [constant X]
genEpsilon = Normal(0, 1)
epsilon = rand(genEpsilon,N)
trueParams = [-K/2:K/2]*.02
Y = X*trueParams + epsilon
params0 = [trueParams,1]

function loglike(rho,x,y)
    beta = rho[1:K+1]
    sigma2 = exp(rho[K+2])
    residual = y-x*beta
    dist = Normal(0, sigma2)
    contributions = logpdf(dist,residual)
    loglikelihood = sum(contributions)
    return -loglikelihood
end

function bootstrapSamples(B)
    println("hi")
    M=convert(Int,floor(N/2))
    samples = zeros(B,K+2)
    for b=1:B
        theIndex = randperm(N)[1:M]
        x = X[theIndex,:]
        y = Y[theIndex,:]
        function wrapLoglike(rho)
            return loglike(rho,x,y)
        end
        samples[b,:] = optimize(wrapLoglike,params0,method=:cg).minimum
    end
    samples[:,K+2] = exp(samples[:,K+2])
    println("bye")
    return samples
end

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