98 citations · 196 across the 32 of their papers we have counts for
3 papers · 1 filter
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional inp…
Bayesian forecasting of mortality rates using latent Gaussian models
Angelos Alexopoulos, Petros Dellaportas, Jonathan J. Forster
We provide forecasts for mortality rates by using two different approaches. First we employ dynamic non-linear logistic models based on Heligman-Pollard formula. Second, we assume…
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
Marcel Hirt, Petros Dellaportas
We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provide…