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02/09/2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Autoencoders and their variants are among the most widely used models in...
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12/06/2021
Statistical implications of relaxing the homogeneous mixing assumption in time series Susceptible-Infectious-Removed models
Infectious disease epidemiologists routinely fit stochastic epidemic mod...
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06/15/2021
A Horseshoe Pit mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging
Finding parsimonious models through variable selection is a fundamental ...
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02/23/2021
Brain Waves Analysis Via a Non-parametric Bayesian Mixture of Autoregressive Kernels
The standard approach to analyzing brain electrical activity is to exami...
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01/11/2021
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
Due to the importance of uncertainty quantification (UQ), Bayesian appro...
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04/20/2020
Optimal Experimental Design for Mathematical Models of Hematopoiesis
The hematopoietic system has a highly regulated and complex structure in...
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02/08/2020
Conjoined Dirichlet Process
Biclustering is a class of techniques that simultaneously clusters the r...
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10/13/2019
Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method
It is well established that temporal organization is critical to memory,...
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10/13/2019
Deep Markov Chain Monte Carlo
We propose a new computationally efficient sampling scheme for Bayesian ...
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05/24/2019
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Dynamic functional connectivity, as measured by the time-varying covaria...
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07/26/2018
A Flexible Joint Longitudinal-Survival Modeling Framework for Incorporating Multiple Longitudinal Biomarkers
We are interested in survival analysis of hemodialysis patients for whom...
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07/06/2018
A Bayesian Framework for Non-Collapsible Models
In this paper, we discuss the non-collapsibility concept and propose a n...
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07/06/2018
A Flexible Joint Longitudinal-Survival Model for Analysis of End-Stage Renal Disease Data
We propose a flexible joint longitudinal-survival framework to examine t...
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11/15/2017
Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States
Motivated by the problem of predicting sleep states, we develop a mixed ...
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11/14/2017
Neural Network Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is a widely used algorithm for sampling from pos...
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11/08/2017
Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices
Modeling correlation (and covariance) matrices is a challenging problem ...
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06/14/2016
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction
We reframe linear dimensionality reduction as a problem of Bayesian infe...
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02/06/2016
Variational Hamiltonian Monte Carlo via Score Matching
Traditionally, the field of computational Bayesian statistics has been d...
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06/19/2015
Sampling constrained probability distributions using Spherical Augmentation
Statistical models with constrained probability distributions are abunda...
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06/18/2015
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
For big data analysis, high computational cost for Bayesian methods ofte...
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02/11/2015
Dependent Matérn Processes for Multivariate Time Series
For the challenging task of modeling multivariate time series, we propos...
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01/19/2012