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- Bayesian modeling, theory, and computation; Even Bill Gates talks about Bayesian ideas!
- Bayesian model selections, variable selections, and neuronized priors.
- Statistical learning methods: factor models, index models, variation methods, Gaussian process regression, etc.
- High-dimensional methods: FDR control methods for linear and nonlinear methods, sliced inverse regression.
- Nonparametric modeling and testing: two-sample test, interaction test, generative bootstrap sampler.
- Statistical missing data problems, imputation methodology, causal inference.
- Gibbs sampling and other MCMC methods. See my book (sample chapters: introduction, Metropolis, and HMC)on the topics.
- Monte Carlo filters, Sequential Monte Carlo; see an overview of Liu & Chen (1998), and its broad applications in Chap 4 of my book .
- Computational Biology: gene expression, cancer immunology, single-cell analysis, statistical genetics
- Computational Biology: sequence and structural analyses; Bayesian evolutionary modeling.