AutomaticMALA

This is a simple implementation of "AutoMALA: Locally Adaptive Metropolis-Adjusted Langevin Algorithm" by Biron-Lattes, Surjanovic & Syed et al. (2023), i.e. MALA but with a step size that is allowed to change in a reversible manner.

Note that this is mainly meant as a demonstration of how to implement samplers in AbstractMCMC.jl and rather than a production ready sampler implementation.

Usage

It is easy to use AutoMALA with Turing.jl through the Turing.externalsampler interface:

using AutomaticMALA, Turing

# Define the model using Turing.jl.
@model function demo()
    s ~ InverseGamma(2, 3)
    m ~ Normal(0, sqrt(s))
    1.5 ~ Normal(m, sqrt(s))
    2.0 ~ Normal(m, sqrt(s))
end
model = demo()

# Set up the sampler.
num_adapts = 1000
sampler = AutoMALA(ϵ_init=1.0, num_unadjusted=num_adapts)

# Perform inference.
chain = sample(model, externalsampler(sampler), 1000; discard_initial=num_adapts)