6 citations · 7 across the 2 of their papers we have counts for
4 papers
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Geoffrey Roeder, Paul K. Grant, Andrew Phillips +2
We introduce a flexible, scalable Bayesian inference framework for nonlinear dynamical systems characterised by distinct and hierarchical variability at the individual, group, and…
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds +3
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probab…
Automatic Variational ABC
Alexander Moreno, Tameem Adel, Edward Meeds +2
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appeali…
Hamiltonian ABC
Edward Meeds, Robert Leenders, Max Welling
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood…