Accelerating astronomical and cosmological inference with preconditioned Monte Carlo

We introduce preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial geometry. PMC utilizes a Normalizing Flow (NF) in order to decorrelate the parameters of the distribution and then proceeds by sampling from the preconditioned target distribution using an adaptive Sequential Monte Carlo (SMC) scheme. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence that can be used for parameter inference and model comparison, respectively. The aforementioned framework has been thoroughly tested in a variety of challenging target distributions achieving state-of-the-art sampling performance. In the cases of primordial feature analysis and gravitational wave inference, PMC is approximately 50 and 25 times faster, respectively, than nested sampling (NS). We found that in higher dimensional applications, the acceleration is even greater. Finally, PMC is directly parallelisable, manifesting linear scaling up to thousands of CPUs.

Automated summary

Preconditioned Monte Carlo (PMC) is an efficient Monte Carlo method for Bayesian inference, which allows for efficient sampling of complex probability distributions. PMC utilizes a Normalizing Flow (NF) to decorrelate the distribution parameters and employs an adaptive Sequential Monte Carlo (SMC) scheme for sampling. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence, which can be used for parameter inference and model comparison.