The SMC2 algorithm is proposed, a sequential Monte Carlo algorithm, defined in the θ‐dimension, which propagates and resamples many particle filters in the x‐ dimension, which explores the applicability of the algorithm in both sequential and non‐sequential applications and considers various degrees of freedom.Expand

This work proposes to avoid the use of summaries and the ensuing loss of information by instead using the Wasserstein distance between the empirical distributions of the observed and synthetic data, and generalizes the well‐known approach of using order statistics within approximate Bayesian computation to arbitrary dimensions.Expand

This work analyzes two alternative schemes that do not involve a collective operation, and compares them to standard schemes, finding that, in certain circumstances, the alternative resamplers can perform significantly faster on a GPU, and to a lesser extent on a CPU, than the standard approaches.Expand

The theoretical validity of the proposed couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee (2014) is established and their efficiency relative to the underlying MCMC algorithms is studied.Expand

These results cover the misspecified setting, in which the data-generating process is not assumed to be part of the family of distributions described by the model, and some difficulties arising in the numerical approximation of these estimators are discussed.Expand

The theoretical validity of the estimators proposed and their efficiency relative to the underlying MCMC algorithms are established and the performance and limitations of the method are illustrated.Expand

This work uses Wasserstein distances between empirical distributions of observed data and empirical distribution of synthetic data drawn from such models to estimate their parameters, and proposes an alternative distance using the Hilbert space-filling curve.Expand

This work combines a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing, and establishes the validity of the proposed estimators under mild assumptions.Expand

Improvements to the independent Metropolis–Hastings algorithm that significantly decrease the variance of any estimator derived from the MCMC output, at a null computing cost since those improvements are based on a fixed number of target density evaluations that can be produced in parallel.Expand