Abstract Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold
standard technique for Bayesian inference. They are theoretically well-understood and …

Deep learning is gaining increasing popularity for spatiotemporal forecasting. However,
prior works have mostly focused on point estimates without quantifying the uncertainty of the …

This paper focuses on inferring a general class of hidden Markov models (HMMs) using data
acquired from experts. Expert-acquired data contain decisions/actions made by …

Bayesian models are powerful tools for studying complex data, allowing the analyst to
encode rich hierarchical dependencies and leverage prior information. Most importantly …

Abstract Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in
Bayesian neural networks (BNNs). This paper initially reviews the main challenges in …

Abstract Stochastic gradient Markov Chain Monte Carlo (SGMCMC) is a scalable algorithm
for asymptotically exact Bayesian inference in parameter-rich models, such as Bayesian …

State space models (SSMs) are a flexible approach to modeling complex time series.
However, inference in SSMs is often computationally prohibitive for long time series …

The widespread use of Deep Neural Networks (DNNs) has recently resulted in their
application to challenging scientific visualization tasks. While advanced DNNs demonstrate …

Ubiquitous applications of Deep neural networks (DNNs) in different artificial intelligence
systems have led to their adoption in solving challenging visualization problems in recent …

State space models (SSMs) provide a flexible framework for modeling complex time series
via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled …