12 citations · 16 across the 11 of their papers we have counts for
11 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…
Time-Varying Multi-Seasonal ARMA Models
Ganna Fagerberg, Mattias Villani, Robert Kohn
We propose an ARMA model that allows for multiple seasonal periods and time varying parameters in both regular and seasonal components, building upon previous work for pure AR proc…
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…
A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection
Linduni M. Rodrigo, Robert Kohn, Hadi M. Afshar +1
This paper introduces a novel Bayesian approach for variable selection in high-dimensional and potentially sparse regression settings. Our method replaces the indicator variables i…
Spectral domain likelihoods for Bayesian inference in time-varying parameter models
Oskar Gustafsson, Mattias Villani, Robert Kohn
Inference for locally stationary processes is often based on some local Whittle-type approximation of the likelihood function defined in the frequency domain. The main reasons for…
Time-Varying Multi-Seasonal AR Models
Ganna Fagerberg, Mattias Villani, Robert Kohn
We propose a seasonal AR model with time-varying parameter processes in both the regular and seasonal parameters. The model is parameterized to guarantee stability at every time po…
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…
Calibrated Generalized Bayesian Inference
David T. Frazier, Christopher Drovandi, Robert Kohn
We propose a simple approach that provides accurate uncertainty quantification for Bayesian inference in misspecified or approximate models, and for generalized (Gibbs) posteriors.…
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…