Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

Abstract

We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is deterministic. For each instantiation of u we run an optimization procedure to minimize the distance between summary statistics of the simulator and the data. After reweighing these samples using the prior and the Jacobian (accounting for the change of volume in transforming from the space of summary statistics to the space of parameters) we show that this weighted ensemble represents a Monte Carlo estimate of the posterior distribution. The procedure can be run embarrassingly parallel (each node handling one sample) and anytime (by allocating resources to the worst performing sample). The procedure is validated on six experiments.

NIPS 2015 camera ready

References in corpus (3)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
• Asynchronous Anytime Sequential Monte Carlo

Cited by in corpus (7)

• The frontier of simulation-based inference
• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
• Neural Density Estimation and Likelihood-free Inference
• Anytime Monte Carlo
• Robust Optimisation Monte Carlo
• Likelihood-free approximate Gibbs sampling
• Extending the statistical software package Engine for Likelihood-Free Inference