Regression with Student-t noise

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We load all the necessary packages

using AbstractGPs
using ApproximateGPs
using AugmentedGPLikelihoods
using Distributions
using LinearAlgebra

Plotting libraries

using Plots

We create some random data (sorted for plotting reasons)

N = 100
x = range(-10, 10; length=N)
kernel = with_lengthscale(SqExponentialKernel(), 2.0)
gp = GP(kernel)
ν = 3.5
σ = 2.0
lik = StudentTLikelihood(ν, σ)
lf = LatentGP(gp, lik, 1e-6)
f, y = rand(lf(x));

We plot the sampled data

plt = scatter(x, y; label="Data")
plot!(plt, x, f; color=:red, label="Latent GP")

CAVI Updates

We write our CAVI algorithmm

function u_posterior(fz, m, S)
    return posterior(SparseVariationalApproximation(Centered(), fz, MvNormal(m, S)))
end

function cavi!(fz::AbstractGPs.FiniteGP, x, y, m, S, qΩ; niter=10)
    K = ApproximateGPs._chol_cov(fz)
    for _ in 1:niter
        post_u = u_posterior(fz, m, S)
        post_fs = marginals(post_u(x))
        aux_posterior!(qΩ, lik, y, post_fs)
        S .= inv(Symmetric(inv(K) + Diagonal(only(expected_auglik_precision(lik, qΩ, y)))))
        m .= S * (only(expected_auglik_potential(lik, qΩ, y)) + K \ mean(fz))
    end
    return m, S
end;

Now we just initialize the variational parameters

m = zeros(N)
S = Matrix{Float64}(I(N))
qΩ = init_aux_posterior(lik, N)
fz = gp(x, 1e-8);

And visualize the current posterior

x_te = -10:0.01:10
plot!(
    plt, x_te, u_posterior(fz, m, S); color=:blue, alpha=0.3, label="Initial VI Posterior"
)

We run CAVI for 3-4 iterations

cavi!(fz, x, y, m, S, qΩ; niter=4);

And visualize the obtained variational posterior

plot!(
    plt,
    x_te,
    u_posterior(fz, m, S);
    color=:darkgreen,
    alpha=0.3,
    label="Final VI Posterior",
)

ELBO

How can one compute the Augmented ELBO? Again AugmentedGPLikelihoods provides helper functions to not have to compute everything yourself

function aug_elbo(lik, u_post, x, y)
    qf = marginals(u_post(x))
    qΩ = aux_posterior(lik, y, qf)
    return expected_logtilt(lik, qΩ, y, qf) - aux_kldivergence(lik, qΩ, y) -
           kldivergence(u_post.approx.q, u_post.approx.fz) # approx.fz is the prior and approx.q is the posterior
end

aug_elbo(lik, u_posterior(fz, m, S), x, y)

-332.3931458838723

Gibbs Sampling

We create our Gibbs sampling algorithm (we could do something fancier with AbstractMCMC)

function gibbs_sample(fz, f, Ω; nsamples=200)
    K = ApproximateGPs._chol_cov(fz)
    Σ = zeros(length(f), length(f))
    μ = zeros(length(f))
    return map(1:nsamples) do _
        aux_sample!(Ω, lik, y, f)
        Σ .= inv(Symmetric(inv(K) + Diagonal(only(auglik_precision(lik, Ω, y)))))
        μ .= Σ * (only(auglik_potential(lik, Ω, y)) + K \ mean(fz))
        rand!(MvNormal(μ, Σ), f)
        return copy(f)
    end
end;

We initialize our random variables

f = randn(N)
Ω = init_aux_variables(lik, N);

Run the sampling for default number of iterations (200)

fs = gibbs_sample(fz, f, Ω);

And visualize the samples overlapped to the variational posterior that we found earlier.

for f in fs
    plot!(plt, x, f; color=:black, alpha=0.07, label="")
end
plt

Package and system information

Status `~/work/AugmentedGPLikelihoods.jl/AugmentedGPLikelihoods.jl/examples/studentt/Project.toml`
  [99985d1d] AbstractGPs v0.5.21
  [298c2ebc] ApproximateGPs v0.4.5
  [4689c64d] AugmentedGPLikelihoods v0.4.18 `/home/runner/work/AugmentedGPLikelihoods.jl/AugmentedGPLikelihoods.jl#main`
  [31c24e10] Distributions v0.25.109
  [98b081ad] Literate v2.19.0
  [91a5bcdd] Plots v1.40.5

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
Environment:
  JULIA_PKG_SERVER_REGISTRY_PREFERENCE = eager
  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src

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