Autoencoders and their variants are among the most widely used models in...
Infectious disease epidemiologists routinely fit stochastic epidemic mod...
Finding parsimonious models through variable selection is a fundamental
...
The standard approach to analyzing brain electrical activity is to exami...
Due to the importance of uncertainty quantification (UQ), Bayesian appro...
The hematopoietic system has a highly regulated and complex structure in...
Biclustering is a class of techniques that simultaneously clusters the r...
It is well established that temporal organization is critical to memory,...
We propose a new computationally efficient sampling scheme for Bayesian
...
Dynamic functional connectivity, as measured by the time-varying covaria...
We are interested in survival analysis of hemodialysis patients for whom...
In this paper, we discuss the non-collapsibility concept and propose a n...
We propose a flexible joint longitudinal-survival framework to examine t...
Motivated by the problem of predicting sleep states, we develop a mixed
...
Hamiltonian Monte Carlo is a widely used algorithm for sampling from
pos...
Modeling correlation (and covariance) matrices is a challenging problem ...
We reframe linear dimensionality reduction as a problem of Bayesian infe...
Traditionally, the field of computational Bayesian statistics has been
d...
Statistical models with constrained probability distributions are abunda...
For big data analysis, high computational cost for Bayesian methods ofte...
For the challenging task of modeling multivariate time series, we propos...
In this paper, we discuss an extension of the Split Hamiltonian Monte Ca...