1 citations · 1 across the 1 of their papers we have counts for
2 papers
stat.CO2026★ 1 cited
Demonstrating the power and flexibility of variational assumptions for amortized neural posterior estimation in environmental applications
Elliot Maceda, Emily C. Hector, Amanda Lenzi +1
Classic Bayesian methods with complex models are frequently infeasible due to an intractable likelihood. Simulation-based inference methods, such as Approximate Bayesian Computing…
stat.ME2024
When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer
Emily C. Hector, Amanda Lenzi
Black-box methods such as deep neural networks are exceptionally fast at obtaining point estimates of model parameters due to their amortisation of the loss function computation, b…