Search Results for author: Edwin Fong

Found 5 papers, 1 papers with code

On the marginal likelihood and cross-validation

no code implementations21 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.

Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates

no code implementations13 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.

Density Estimation

Martingale Posterior Neural Processes

no code implementations19 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.

Bayesian Inference Gaussian Processes

Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling

no code implementations12 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.

Transfer Learning

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