EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Tom Ryder

PhD title

Variational inference with applications to stochastic processes

Variational Inference provides a way to evaluate and sample from hard-to-compute probability densities. With the modern advances in GPU technologies, such techniques are faster and more powerful than ever before.

Using variational inference, we perform full probabilistic inference, beginning on stochastic differential equations, and approximate the conditioned diffusion process and parameter posteriors.


Dennis Prangle


Black-Box Variational Inference for Stochastic Differential Equations - Ryder, T. Golightly, A. McGough, S. Prangle, D. - 35th International Conference on Machine Learning - Stockholm, Sweden - July 2018

Black-Box Autoregressive Density Estimation for State-Space Models - Ryder, T. Golightly, A. McGough, S. Prangle, D. - Bayesian Deep Learning workshop, NeurIPS 2018 - November 2018

Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes - Ryder, T. Ward, W.O. Prangle, D. Alvarez, A.M. - 2019 Conference on Neural Information Processing Systems - Vancouver, Canada - December 2019

Scalable approximate inference for state space models with normalising flows - Ryder, T. Golightly, A. Matthews, I. Prangle, D. - October 2019

Variational Inference for Diffusion Processes - Invited Speaker - 11th Workshop on Bayesian Inference in Stochastic Processes - Madrid, Spain - June 2019