Overview
Wing Hung Wong is a statistician (Stanford University) known for foundational contributions to Bayesian computation, Monte Carlo methods, and statistical genomics. With Martin Tanner he co-invented data augmentation; he later contributed key results on Markov chain Monte Carlo convergence and importance/bridge sampling.
Key Contributions / Features
- Data augmentation — Tanner–Wong (1987): With Martin Tanner, introduced the data-augmentation algorithm for computing posteriors via latent variables — the Bayesian analogue of EM and a precursor of the Gibbs sampler. See Tanner-Wong (1987) and Data Augmentation.
- MCMC theory (Liu–Wong–Kong 1994): Covariance structure and convergence of the Gibbs sampler and data-augmentation Markov chains.
- Bridge sampling (Meng–Wong 1996): Simulating normalizing constants / ratios via bridge sampling, central to marginal-likelihood estimation.
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