Abstract Bayesian model selection provides a powerful framework for objectively comparing models directly from observed data, without reference to ground truth data. However, requires the computation of marginal likelihood (model evidence), which is computationally challenging, prohibiting its use in many high-dimensional inverse problems. With imaging applications mind, this work we present pr...

a r t i c l e i n f o a b s t r a c t A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibra...

Estimating nested expectations is an important task in computational mathematics and statistics. In this paper we propose a new Monte Carlo method using post-stratification to estimate efficiently without taking samples of the inner random variable from conditional distribution given outer variable. This property provides advantage over many existing methods that it enables us only with dataset...

We present a novel method for sampling iso-likelihood contours in nested using type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed problems where computing the likelihood computationally expensive therefore cost training flow offset by overall reduction number evaluations. validate on 128 simulated gravitational wave signa...

In order to reconcile petrological and geophysical observations of magmatic processes in the temporal domain, uncertainties diffusion timescales need be rigorously assessed. Here, we present a new chronometry method: Diffusion using Finite Elements Nested Sampling (DFENS). This method combines finite element numerical model with nested sampling Bayesian inversion, meaning that parameters contri...

The purpose of this paper is to provide a typology of sampling designs for qualitative researchers. We introduce the following sampling strategies: (a) parallel sampling designs, which represent a body of sampling strategies that facilitate credible comparisons of two or more different subgroups that are extracted from the same levels of study; (b) nested sampling designs, which are sampling st...