22 citations · 33 across the 10 of their papers we have counts for
11 papers
Asymptotics for parametric martingale posteriors
Edwin Fong, Andrew Yiu
The martingale posterior framework is a generalization of Bayesian inference where one elicits a sequence of one-step ahead predictive densities instead of the likelihood and prior…
Bayesian Quantile Estimation and Regression with Martingale Posteriors
Edwin Fong, Andrew Yiu
Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian n…
Semiparametric posterior corrections
Andrew Yiu, Edwin Fong, Chris Holmes +1
We present a new approach to semiparametric inference using corrected posterior distributions. The method allows us to leverage the adaptivity, regularization and predictive power…
Martingale Posterior Neural Processes
Hyungi Lee, Eunggu Yun, Giung Nam +2
A Neural Process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussi…
Causal predictive inference and target trial emulation
Andrew Yiu, Edwin Fong, Stephen Walker +1
Causal inference from observational data can be viewed as a missing data problem arising from a hypothetical population-scale randomized trial matched to the observational study. T…
Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates
Sahra Ghalebikesabi, Chris Holmes, Edwin Fong +1
Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization effect induced by the prior. In the context of density estimation, t…
A Predictive Approach to Bayesian Nonparametric Survival Analysis
Edwin Fong, Brieuc Lehmann
Bayesian nonparametric methods are a popular choice for analysing survival data due to their ability to flexibly model the distribution of survival times. These methods typically e…
Conformal Bayesian Computation
Edwin Fong, Chris Holmes
We develop scalable methods for producing conformal Bayesian predictive intervals with finite sample calibration guarantees. Bayesian posterior predictive distributions, $p(y \mid…
Martingale posterior distributions
Edwin Fong, Chris Holmes, Stephen G. Walker
The prior distribution on parameters of a sampling distribution is the usual starting point for Bayesian uncertainty quantification. In this paper, we present a different perspecti…
On the marginal likelihood and cross-validation
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. In co…
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong, Simon Lyddon, Chris Holmes
Increasingly complex datasets pose a number of challenges for Bayesian inference. Conventional posterior sampling based on Markov chain Monte Carlo can be too computationally inten…