21 citations · 41 across the 5 of their papers we have counts for
5 papers
A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
David J. Schodt, Ryan Brown, Michael Merritt +3
Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications. Often, heteroscedastic aleatoric uncertainties are learned as…
Simulation Approaches to General Probabilistic Inference on Belief Networks
Ross D. Shachter, Mark Alan Peot
A number of algorithms have been developed to solve probabilistic inference problems on belief networks. These algorithms can be divided into two main groups: exact techniques whic…
Decision Making Using Probabilistic Inference Methods
Ross D. Shachter, Mark Alan Peot
The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilist…
Geometric Implications of the Naive Bayes Assumption
Mark Alan Peot
A naive (or Idiot) Bayes network is a network with a single hypothesis node and several observations that are conditionally independent given the hypothesis. We recently surveyed a…
Learning From What You Don't Observe
Mark Alan Peot, Ross D. Shachter
The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algori…