9 papers
Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes
Percy S. Zhai, Max Pachman, Peverel D. Shipley +1
Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows. We consider learning sufficient summary statistics…
Possibilistic Radial Transport for Approximate IM Inference
Jungeum Kim, Percy Zhai
Probing the hypothesis space after seeing the data remains valid under possibilistic inferential models (IMs), provided the significance level stays fixed. The price is computation…
Conformal Coverage of Time Series: Validity and Inference
Percy S. Zhai, Maggie Cheng, Wei Biao Wu
Conformal prediction provides marginal coverage guarantees, yet practitioners may wonder if the observed coverage is truly abnormal or consistent with sampling variation. Inference…
Valid and Efficient Split Conformal Regression for Time Series
Percy S. Zhai, Maggie Cheng, Wei Biao Wu
We study conformalized quantile regression and conformalized median regression that fit a model on one block of a time series and calibrate the conformal interval on the adjacent b…
High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series
Percy S. Zhai, Ping-Shou Zhong, Wei Biao Wu
Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied. We develop a direct, data-adaptive test…
Horseshoe Predictive Inference
Percy S. Zhai, Veronika Ročková
Predictive inference in the sparse Gaussian sequence model has received considerably less attention than its non-sparse, finite-sample counterpart. Existing work has largely been c…
Simultaneous Inference for Covariance and Precision Matrices of Long-Range Dependent Time Series
Percy S. Zhai, Mladen Kolar, Wei Biao Wu
For time series with long-range temporal dependence, inference for covariance and precision matrices is non-trivial. We propose a Berry-Esseen type Gaussian approximation result th…
Conditional Flow Matching for Bayesian Posterior Inference
Percy S. Zhai, So Won Jeong, Veronika Ročková
We propose a generative multivariate posterior sampler via flow matching. It offers a simple training objective, and does not require access to likelihood evaluation. The method le…
Deep Generative Quantile Bayes
Jungeum Kim, Percy S. Zhai, Veronika Ročková
We develop a multivariate posterior sampling procedure through deep generative quantile learning. Simulation proceeds implicitly through a push-forward mapping that can transform i…