6 papers
Physics-Informed Learning for Robust Acoustic Localization with Calibrated Uncertainty
Jennifer N. Kampe, Changwoo J. Lee, Xin Shen +5
Recent advances in Passive Acoustic Monitoring (PAM) offer an opportunity to obtain ecological spatial point-process data at unprecedented scale. However, realizing this opportunit…
ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing
Xin Shen, Jennifer N. Kampe, Changwoo J. Lee +7
Microphone array-based passive acoustic monitoring is increasingly used for biodiversity sensing in forests. However, design and evaluation of array systems and configurations rema…
Discovering Spatial Patterns of Readmission Risk Using a Bayesian Competing Risks Model with Spatially Varying Coefficients
Yueming Shen, Christian Pean, David Dunson +1
Time-to-event models are commonly used to study associations between risk factors and disease outcomes in the setting of electronic health records (EHR). In recent years, focus has…
Variational bagging: a robust approach for Bayesian uncertainty quantification
Shitao Fan, Ilsang Ohn, David Dunson +1
Variational Bayes methods are popular due to their computational efficiency and adaptability to diverse applications. In specifying the variational family, mean-field classes are c…
Efficient Bayesian Inference for Discretely Observed Continuous Time Markov Chains
Tao Tang, Lachlan Astfalck, David Dunson
Inference for continuous-time Markov chains (CTMCs) becomes challenging when the process is only observed at discrete time points. The exact likelihood is intractable, and existing…
Adaptive Bayesian Regression on Data with Low Intrinsic Dimensionality
Tao Tang, Nan Wu, Xiuyuan Cheng +1
We study how the posterior contraction rate under a Gaussian process (GP) prior depends on the intrinsic dimension of the predictors and the smoothness of the regression function.…