5 papers
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…
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…
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…