abctools: An R Package for Tuning Approximate Bayesian Computation Analyses. - Matthew A. Nunes; Dennis Prangle (R J., 2015)

Lazy ABC. - Dennis Prangle (Stat. Comput., 2016)

Marginal sequential Monte Carlo for doubly intractable models. - Richard G. Everitt; Dennis Prangle; Philip Maybank; Mark Bell (CoRR, 2017)

Black-Box Variational Inference for Stochastic Differential Equations. - Thomas Ryder; Andrew Golightly; A. Stephen McGough; Dennis Prangle (ICML, 2018)

Black-Box Autoregressive Density Estimation for State-Space Models. - Tom Ryder; Andrew Golightly; A. Stephen McGough; Dennis Prangle (CoRR, 2018)

Recalibration: A post-processing method for approximate Bayesian computation. - G. S. Rodrigues; Dennis Prangle; Scott A. Sisson (Comput. Stat. Data Anal., 2018)

A rare event approach to high-dimensional approximate Bayesian computation. - Dennis Prangle; Richard G. Everitt; Theodore Kypraios (Stat. Comput., 2018)

Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes. - Wil O. C. Ward; Tom Ryder; Dennis Prangle; Mauricio A. Álvarez (CoRR, 2019)

Scalable approximate inference for state space models with normalising flows. - Tom Ryder; Andrew Golightly; Isaac Matthews; Dennis Prangle (CoRR, 2019)

Black-Box Inference for Non-Linear Latent Force Models. - Wil O. C. Ward; Tom Ryder; Dennis Prangle; Mauricio A. Álvarez (AISTATS, 2020)

Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners. - Rob Geada; Dennis Prangle; Andrew Stephen McGough (CoRR, 2020)

gk: An R Package for the g-and-k and Generalised g-and-h Distributions. - Dennis Prangle (R J., 2020)

Measure Transport with Kernel Stein Discrepancy. - Matthew Fisher; Tui Nolan; Matthew M. Graham; Dennis Prangle; Chris J. Oates (AISTATS, 2021)

Optimising Fairness Through Parametrised Data Sampling. - Vladimiro González-Zelaya; Julián Salas; Dennis Prangle; Paolo Missier (EDBT, 2021)

The neural moving average model for scalable variational inference of state space models. - Thomas Ryder; Dennis Prangle; Andrew Golightly; Isaac Matthews (UAI, 2021)

Preprocessing Matters: Automated Pipeline Selection for Fair Classification. - Vladimiro González-Zelaya; Julián Salas; Dennis Prangle; Paolo Missier (MDAI, 2023)

Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data. - Tennessee Hickling; Dennis Prangle (PKDD/ECML Workshops (5), 2023)

Distilling Importance Sampling for Likelihood Free Inference. - Dennis Prangle; Cecilia Viscardi (J. Comput. Graph. Stat., 2023)

Flexible Tails for Normalizing Flows. - Tennessee Hickling; Dennis Prangle (CoRR, 2024)