Generate R.V.

An important Lemma.

Note: Unless otherwise stated, the algorithms and the corresponding screenshots are adopted from Robert and Casella (2005).

Box-Muller Algorithm

function boxmuller()
    x = ones(2)
    u = rand(2)
    logu = sqrt(-2*log(u[1]))
    x[1] = logu * cos(2*pi*u[2])
    x[2] = logu * sin(2*pi*u[2])
    return(x)
end

## example
boxmuller()

Accept-Reject Method

Example:

The Julia code is as follows:

function AccRej(f::Function, M)
    ## use normal distribution N(0, 1) as sampling function g
    while true
        x = randn()
        u = rand()
        cutpoint = f(x)/(M*g(x))
        if u <= cutpoint
            return([x, f(x)])
        end
    end
end

## density function of N(0, 1)
function g(x)
    return(exp(-0.5*x^2)/sqrt(2*pi))
end

## example function and ignore the normalized constant
function f(x)
    return(exp(-x^2/2)*(sin(6*x)^2 + 3*cos(x)^2*sin(4*x)^2 + 1))
end

## example
N = 500;
data = ones(N, 2);
for i = 1:500
    data[i,:] = AccRej(f, sqrt(2*pi)*5)
end

Envelope Accept-Reject

It is easy to write the Julia code:

function EnvAccRej(f::function, M, gl::function)
    while true
        x = randn() # still assume gm is N(0,1)
        u = rand()
        cutpoint1 = gl(x)/(M*g(x))
        cutpoint2 = f(x)/(M*g(x))
        if u <= cutpoint1
            return(x)
        elseif u <= cutpoint2
            return(x)
        end
    end
end

Atkinson's Poisson Simulation

It is necessary to note that the parameters in the algorithm are not same with those in the density function. In other words, the corresponding density function of the algorithm should be

function AtkinsonPois(lambda)
    # parameters
    beta = pi/sqrt(3*lambda)
    alpha = lambda*beta
    c = 0.767 - 3.36/lambda
    k = log(c) - lambda - log(beta)
    # step 1: propose new x
    u1 = rand()
    while true
        global x
        x = (alpha - log((1-u1)/u1))/beta
        x > -0.5 && break
    end
    while true
        # step 2: transform to N
        N = floor(Int, x)
        u2 = rand()
        # step 3: accept or not
        lhs = alpha - beta*x + log(u2/(1+exp(alpha-beta*x))^2)
        rhs = k + N*log(lambda) - log(factorial(lambda))
        if lhs <= rhs
            return(N)
        end
    end
end

## example
N = 100;
res = ones(Int, N);
for i = 1:N
    res[i] = AtkinsonPois(10)
end
# ans: 8 9 13 10 12 .......

As mentioned in the above exercise, another poisson generation method can be derived from the following exercise.

We can write the following Julia code to implement this generation procedure.

function SimplePois(lambda)
    s = 0
    k = 0
    while true
        u = rand()
        x = -log(u)/lambda
        s = s + x
        if s > 1
            return(k)
        end
        k = k + 1
    end
end

## example for simple poisson
res2 = ones(Int, N);
for i = 1:N
    res2[i] = SimplePois(10)
end
# ans: 5 7 16 7 10 .......

ARS Algorithm

the envelopes are

Davison (2008) provides another version of ARS

and gives an illustration example.

and

# example 3.22 in Davison(2008)
r = 2; m = 10; mu = 0; sig2 = 1;
# range of y
yl = -2; yu = 2;
# function of h
function h(y)
    return(r * y - m * log(1 + exp(y)) - (y - mu)^2 / (2 * sig2))
end

function h(y::Array)
    return(r * y - m * log.(1 .+ exp.(y)) .- (y .- mu) .^2 ./ (2 * sig2))
end

# derivative of h
function dh(y)
    return(r - m * exp(y) / (1 + exp(y)) - (y - mu) / sig2)
end

function dh(y::Array)
    return(r .- m * exp.(y) ./ (1 .+ exp.(y)) .- (y .- mu)./sig2)
end

# intersection point
function zfix(yfixed::Array)
    yf0 = yfixed[1:end-1]
    yf1 = yfixed[2:end]
    zfixed = yf0 .+ (h(yf0) .- h(yf1) .+ (yf1 .- yf0) .* dh(yf1)) ./ (dh(yf1) .- dh(yf0))
    return(zfixed)
end

# evaluate log-density (not necessary)
function hplus(y::Float64, yfixed::Array)
    zfixed = zfix(yfixed)
    n = size(zfixed, 1)
    for i = 1:n
        if i == 1 && y < zfixed[i]
            return(h(yfixed[i]) + (y - yfixed[i]) * dh(yfixed[i]))
        elseif i < n && y >= zfixed[i] && y < zfixed[i+1]
            return(h(yfixed[i+1]) + (y - yfixed[i+1]) * dh(yfixed[i+1]))
        elseif i == n && y >= zfixed[n]
            return(h(yfixed[i]) + (y - yfixed[i]) * dh(yfixed[i]))
        end
    end
end

# calculate G_+(z_i)
function gplus_cdf(yfixed::Array, zfixed::Array)
    n = size(zfixed, 1)
    s = zeros(n+1)
    pr = zeros(n+1)
    for i = 1:(n+1)
        ## integral from -infty to zi
        if i == 1
        #    s[i] = exp(h(yfixed[i])) / dh(yfixed[i]) * (exp((zfixed[i]-yfixed[i]) * dh(yfixed[i])) - )
            s[i] = exp(h(yfixed[i])) / dh(yfixed[i]) * (exp((zfixed[i]-yfixed[i]) * dh(yfixed[i])) - exp((yl-yfixed[i]) * dh(yfixed[i])))
        elseif i == n+1
            s[i] = exp(h(yfixed[i])) / dh(yfixed[i]) * (exp((yu-yfixed[i]) * dh(yfixed[i])) - exp((zfixed[n]-yfixed[i]) * dh(yfixed[i])))
        else
            s[i] = exp(h(yfixed[i])) / dh(yfixed[i]) * (exp((zfixed[i]-yfixed[i]) * dh(yfixed[i])) - exp((zfixed[i]-yfixed[i]) * dh(yfixed[i])))
        end
    end
    pr = s / sum(s)
    return cumsum(pr), sum(s)
end

# sample from gplus density
function gplus_sample(yfixed)
    zfixed = zfix(yfixed)
    gp = gplus_cdf(yfixed, zfixed)
    zpr = gp[1]
    norm_const = gp[2]
    n = size(zfixed, 1)
    u = rand()
    # Invert the gplus pdf
    for i = 1:n
        if i == 1 && u < zpr[i]
            ey = u * dh(yfixed[i]) * norm_const / exp(h(yfixed[i])) + exp((yl-yfixed[i])*dh(yfixed[i]))
            return(yfixed[i] + log(ey)/dh(yfixed[i]))    
        elseif i == n && u >= zpr[i]
            ey = (u - zpr[i]) * dh(yfixed[i+1]) * norm_const / exp(h(yfixed[i+1])) + exp((zfixed[i]-yfixed[i+1])*dh(yfixed[i+1]))
            return(yfixed[i+1] + log(ey)/dh(yfixed[i+1]))
        elseif i < n && u >= zpr[i] && u < zpr[i+1]
            ey = (u - zpr[i]) * dh(yfixed[i+1]) * norm_const / exp(h(yfixed[i+1])) + exp((zfixed[i]-yfixed[i+1])*dh(yfixed[i+1]))
            return(yfixed[i+1] + log(ey)/dh(yfixed[i+1]))
        end
    end
end

Back to the main sampling algorithm, we can implement the procedure as follows:

## adaptive rejection sampling
function ars(yfixed::Array)
    x = gplus_sample(yfixed)
    u = rand()
    if u <= exp(h(x)-hplus(x, yfixed))
        return(x)
    else
        return(ars(append!(yfixed, x)))
    end
end

## example
N = 100
res = ones(N);
for i = 1:N
    res[i] = ars([-1.8,-1.1,-0.5,-0.2])
end

Based on the ARS algorithm, we can also get the Supplemental ARS algorithm:

Exponential Random Variable

The inverse transform sampling from a uniform distribution can be easily used to sample an exponential random variable. The CDF is

whose inverse function is

Today, I came across another method from Xi'an's blog, which points to the question on StackExchange.

Still confused about the details, as commented in the code.

The theoretical part is as follows,

I rewrite the provided C code in Julia, and compare the distribution with samples from inverse CDF and the package Distributions

a = [exp_rand() for i=1:1000]
b = [-log(rand()) for i=1:1000]
c = rand(Exponential(1), 1000)
histogram(a, bins=40, label = "sexp")
histogram!(b, bins=40, alpha=0.5, label = "invF")
histogram!(c, bins=40, alpha=0.5, label = "rand")