1 code implementation • 8 Feb 2019 • Edwin Fong, Simon Lyddon, Chris Holmes
Increasingly complex datasets pose a number of challenges for Bayesian inference.
no code implementations • 21 May 2019 • Edwin Fong, Chris Holmes
In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior.
no code implementations • 13 Jun 2022 • Sahra Ghalebikesabi, Chris Holmes, Edwin Fong, Brieuc Lehmann
In the context of density estimation, the standard nonparametric Bayesian approach is to target the posterior predictive of the Dirichlet process mixture model.
no code implementations • 19 Apr 2023 • Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee
Based on this result, instead of assuming any form of the latent variables, we equip a NP with a predictive distribution implicitly defined with neural networks and use the corresponding martingale posteriors as the source of uncertainty.
no code implementations • 12 Mar 2024 • Hyungi Lee, Giung Nam, Edwin Fong, Juho Lee
The nonparametric learning (NPL) method is a recent approach that employs a nonparametric prior for posterior sampling, efficiently accounting for model misspecification scenarios, which is suitable for transfer learning scenarios that may involve the distribution shift between upstream and downstream tasks.