156 citations · 262 across the 16 of their papers we have counts for
18 papers
Spectral divide-and-conquer MCMC for long stationary time series
Zixuan Wang, Matias Quiroz, Feng Li +2
The temporal dependence inherent in time series models poses a fundamental challenge for distributed Bayesian inference, as it precludes embarrassingly parallel algorithms based on…
Spectral subsampling MCMC for Lévy-driven continuous-time ARMA models with expensive likelihood contributions
Thomas Goodwin, Matias Quiroz, Robert Kohn +1
Subsampling-based Markov chain Monte Carlo (MCMC) algorithms aim to accelerate Bayesian inference by evaluating the likelihood using only a subset of the data at each iteration. Ho…
Calibrated Bayesian inference for random fields on large irregular domains using the debiased spatial Whittle likelihood
Thomas Goodwin, Arthur Guillaumin, Matias Quiroz +2
Bayesian inference for stationary random fields is computationally demanding. Whittle-type likelihoods in the frequency domain based on the fast Fourier Transform (FFT) have severa…
Dynamic linear regression models for forecasting time series with semi long memory errors
Thomas Goodwin, Matias Quiroz, Robert Kohn
Dynamic linear regression models forecast the values of a time series based on a linear combination of a set of exogenous time series while incorporating a time series process for…
Analysing symbolic data by pseudo-marginal methods
Yu Yang, Matias Quiroz, Boris Beranger +2
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. The inferen…
A correlated pseudo-marginal approach to doubly intractable problems
Yu Yang, Matias Quiroz, Robert Kohn +1
Doubly intractable models are encountered in a number of fields, e.g. social networks, ecology and epidemiology. Inference for such models requires the evaluation of a likelihood f…
Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes
Mattias Villani, Matias Quiroz, Robert Kohn +1
Spectral subsampling MCMC was recently proposed to speed up Markov chain Monte Carlo (MCMC) for long stationary univariate time series by subsampling periodogram observations in th…
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone, Matias Quiroz, Robert Kohn +2
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datasets has developed rapidly in recent years. However, the underlying methods are generally limited to relativel…
Variance reduction properties of the reparameterization trick
Ming Xu, Matias Quiroz, Robert Kohn +1
The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches s…
Subsampling MCMC - An introduction for the survey statistician
Matias Quiroz, Mattias Villani, Robert Kohn +2
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical i…
Subsampling Sequential Monte Carlo for Static Bayesian Models
David Gunawan, Khue-Dung Dang, Matias Quiroz +2
We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequ…
Gaussian variational approximation for high-dimensional state space models
Matias Quiroz, David J. Nott, Robert Kohn
Our article considers a Gaussian variational approximation of the posterior density in a high-dimensional state space model. The variational parameters to be optimized are the mean…
Hamiltonian Monte Carlo with Energy Conserving Subsampling
Khue-Dung Dang, Matias Quiroz, Robert Kohn +2
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the…
The block-Poisson estimator for optimally tuned exact subsampling MCMC
Matias Quiroz, Minh-Ngoc Tran, Mattias Villani +2
Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is p…
The Block Pseudo-Marginal Sampler
M. -N. Tran, R. Kohn, M. Quiroz +1
The pseudo-marginal (PM) approach is increasingly used for Bayesian inference in statistical models, where the likelihood is intractable but can be estimated unbiasedly. %Examples…
Speeding Up MCMC by Delayed Acceptance and Data Subsampling
Matias Quiroz, Minh-Ngoc Tran, Mattias Villani +1
The complexity of the Metropolis-Hastings (MH) algorithm arises from the requirement of a likelihood evaluation for the full data set in each iteration. Payne and Mallick (2015) pr…
Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator
Matias Quiroz, Mattias Villani, Robert Kohn
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly…
Speeding Up MCMC by Efficient Data Subsampling
Matias Quiroz, Robert Kohn, Mattias Villani +1
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for observations is estimated from a random subset of observations. W…