Independent MH

Gamma distribution

Robert and Casella (2013) presents the following algorithm:

If there exists a constant $M$ such that

f(x) \le Mg(x)\,,\qquad \forall x\in \mathrm{supp}\;f\,,

the algorithm produces a uniformly ergodic chain (Theorem), and the expected acceptance probability associated with the algorithm is at least $1/M$ when the chain is stationary, and in that sense, the IMH is more efficient than the Accept-Reject algorithm.

Let's illustrate this algorithm with ${\mathcal G}a(\alpha, 1)$ . We have introduced how to sample from Gamma distribution via Accept-Reject algorithm in Special Distributions, and it is straightforward to get the Gamma Metropolis-Hastings based on the ratio of $f/g$ ,

And we can implement this algorithm with the Julia code:

function mh_gamma(T = 100, alpha = 1.5)
    a = Int(floor(alpha))
    b = a/alpha
    x = ones(T+1) # initial value: 1
    for t = 1:T
        yt = rgamma_int(a, b)
        rt = (yt / x[t] * exp((x[t] - yt) / alpha))^(alpha-a)
        if rt >= 1
            x[t+1] = yt
        else
            u = rand()
            if u < rt
                x[t+1] = yt
            else
                x[t+1] = x[t]
            end
        end   
    end
    return(x)
end

To sample ${\mathcal G}a(2.43, 1)$ , and estimate $\mathrm{E}_f(X^2)=2.43+2.43^2=8.33$ .

# comparison with accept-reject
res = mh_gamma(5000, 2.43)[2:end]
est = cumsum(res.^2) ./ collect(1:5000)

res2 = ones(5000)
for i = 1:5000
    res2[i] = rgamma(2.43, 1)
end
est2 = cumsum(res2.^2) ./ collect(1:5000)

using Plots
plot(est, label="Independent MH")
plot!(est2, label="Accept-Reject")
hline!([8.33], label="True value")

Logistic Regression

We observe $(x_i,y_i),i=1,\ldots,n$ according to the model

Y_i\sim\mathrm{Bernoulli}(p(x_i))\,,\qquad p(x) = \frac{\exp(\alpha+\beta x)}{1+\exp(\alpha+\beta x)}\,.

The likelihood is

L(\alpha,\beta\mid \mathbf y) \propto \prod_{i=1}^n \Big(\frac{\exp(\alpha+\beta x_i)}{1+\exp(\alpha+\beta x_i)}\Big)^{y_i}\Big(\frac{1}{1+\exp(\alpha+\beta x_i)}\Big)^{1-y_i}

and let $\pi(e^\alpha)\sim \mathrm{Exp}(1/b)$ and put a flat prior on $\beta$ , i.e.,

\pi_\alpha(\alpha\mid b)\pi_\beta(b) = \frac 1b e^{-e^\alpha/b}de^\alpha d\beta=\frac 1b e^\alpha e^{-e^\alpha/b}d\alpha d\beta\,.

Note that

\begin{aligned} \mathrm{E}[\alpha] &= \int_{-\infty}^\infty \frac{\alpha}{b}e^\alpha e^{-e^\alpha/b}d\alpha\\ &=\int_0^\infty \log w\frac 1b e^{-w/b} \\ &=\log b -\gamma\,, \end{aligned}

where

\gamma = -\int_0^\infty e^{-x}\log xdx

is the Euler's Constant.

Choose the data-dependent value that makes $\mathrm{E}\alpha=\hat\alpha$ , where $\hat \alpha$ is the MLE of $\alpha$ , so $\hat b=\exp(\hat \alpha+\gamma)$ .

We can use MLE to estimate the coefficient in the logistical model (see my post for the derivation), and the following Julia code can help us fit the model quickly.

using DataFrames, GLM, Plots

temp = [53, 57, 58, 63, 66, 67, 67, 67, 68, 69, 70, 70, 70, 70, 72, 73, 75, 75, 76, 76, 78, 79, 81]
failure = [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0]
data = DataFrame(temp = temp, failure = failure)
logit_fit = glm(@formula(failure ~ temp), data, Binomial(), LogitLink())
plot(temp, predict(logit_fit), legend = false, xlabel = "Temperature", ylab = "Probability")
scatter!(temp, predict(logit_fit))

The estimates of the parameters are $\hat\alpha=15.0479, \hat\beta=-0.232163$ and $\hat\sigma_\beta = 0.108137$ .

Wrote the following Julia code to implement the independent MH algorithm,

## metropolis-hastings
using Distributions
γ = 0.57721
function ll(α::Float64, β::Float64)
    a = exp.(α .+ β*temp)
    return prod( (a ./ (1 .+ a) ).^failure .* (1 ./ (1 .+ a)).^(1 .- failure) )
end
function mh_logit(T::Int, α_hat::Float64, β_hat::Float64, σ_hat::Float64)
    φ = Normal(β_hat, σ_hat)
    π = Exponential(exp(α_hat+γ))
    Α = ones(T)
    Β = ones(T)
    for t = 1:T-1
        α = log(rand(π))
        β = rand(φ)
        r = ( ll(α, β) / ll(Α[t], Β[t]) ) * ( pdf(φ, β) / pdf(φ, Β[t]) )
        if rand() < r
            Α[t+1] = α
            Β[t+1] = β
        else
            Α[t+1] = Α[t]
            Β[t+1] = Β[t]
        end
    end
    return Α, Β
end

Α, Β = mh_logit(10000, 15.04, -0.233, 0.108)

p1 = plot(Α, legend = false, xlab = "Intercept")
hline!([15.04])

p2 = plot(Β, legend = false, xlab = "Slope")
hline!([-0.233])

plot(p1, p2, layout = (1,2))

Saddlepoint tail area approximation

If $K(\tau) = \log({\mathbb E}\exp(\tau X))$ is the cumulant generating function, solving the saddlepoint equation $K'(\tau)=x$ yields the saddlepoint. For noncentral chi squared random variable $X$ , the moment generating function is

\phi_X(t) = \frac{\exp(2\lambda t/(1-2t))}{(1-2t)^{p/2}}\,,

where $p$ is the number of degrees of freedom and $\lambda$ is the noncentrality parameter, and its saddlepoint is

\hat\tau(x) = \frac{-p+2x-\sqrt{p^2+8\lambda x}}{4x}\,.

The saddlepoint can be used to approximate the tail area of a distribution. We have the approximation

\begin{aligned} P(\bar X>a) &= \int_a^\infty \Big(\frac{n}{2\pi K_X''(\hat\tau(x))}\Big)^{1/2}\exp\{n[K_X(\hat\tau(x))-\hat\tau(x)x]\}dx\\ &= \int_{\hat\tau(a)}^{1/2} \Big(\frac{n}{2\pi}\Big)^{1/2}[K_X''(t)]^{1/2}\exp\{n[K_X(t)-tK_X'(t)]\}dt\\ &\approx \frac 1m\sum_{i=1}^m\mathbb{I}[Z_i>\hat \tau(a)]\,, \end{aligned}

where $Z_i$ is the sample from the saddlepoint distribution. Using a Taylor series approximation,

\exp\{n[K_X(t)-tK_X'(t)]\}\approx \exp\Big\{-nK''_X(0)\frac{t^2}{2}\Big\}\,,

so the instrumental density can be chosen as $N(0,\frac{1}{nK_X''(0)})$ , where

K_X''(t)=\frac{2[p(1-2t) + 4\lambda]}{(1-2t)^3}\,.

I implemented the independent MH algorithm via the following code to produce random variables from the saddlepoint distribution.

using Distributions

p = 6
λ = 9

function K(t)
    return 2λ*t / (1-2t) - p / 2 * log(1-2t)
end

function D1K(t)
    return ( 4λ*t ) / (1-2t)^2 + ( 2λ + p ) / ( 1 - 2t )
end

function D2K(t)
    return 2(p*(1-2t) + 4λ) / (1-2t)^3
end

function logf(n, t)
    return n * (K(t) - t*D1K(t)) + 0.5log(D2K(t))
end

function logg(n, t)
    return -1n * D2K(0) * t^2/2
end

function mh_saddle(T::Int = 10000; n::Int = 1)
    Z = zeros(T)
    g = Normal(0, 1 / sqrt(n*D2K(0)))
    for t = 1:T-1
        z = rand(g)
        logr = logf(n, z) - logf(n, Z[t]) + logg(n, Z[t]) - logg(n, z)
        if log(rand()) < logr 
            Z[t+1] = z
        else
            Z[t+1] = Z[t]
        end
    end
    return Z
end

Z = mh_saddle(n = 1)

function tau(x)
    return ( -1p + 2x - sqrt(p^2 + 8λ * x) ) / (4x)
end

println(sum(Z .> tau(36.225)) / 10000)
println(sum(Z .> tau(40.542)) / 10000)
println(sum(Z .> tau(49.333)) / 10000)

I can successfully reproduce the results.

If $n=1$ ,

Interval

1 - pchisq(x, 6, 9*2)

IMH

$(36.225,\infty)$

0.1000076

0.1037

$(40.542,\infty)$

0.05000061

0.0516

$(49.333,\infty)$

0.01000057

0.0114

For $n=100$ , note that if $X_i\sim \chi^2_p(\lambda)$ , then $n\bar X=\sum X_i\sim \chi^2_{np}(n\lambda)$ , then we can use the following R code to figure out the cutpoints.

qchisq(0.90, 600, 1800)/100
qchisq(0.95, 600, 1800)/100
qchisq(0.99, 600, 1800)/100

Then use these cutpoints to estimate the probability by using samples produced from independent MH algorithm.

Interval

1 - pchisq(x*100, 6*100, 9*2*100)

IMH

$(25.18054,\infty)$

0.10

0.1045

$(25.52361,\infty)$

0.05

0.0516

$(26.17395,\infty)$

0.01

0.0093

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