Year: 2022
Type: preprint
Abstract: The paper presents a new perspective on the mixture of Dirichlet process model which allows the recovery of full and correct uncertainty quantification associated with the full model, even after having integrated out the random distribution function. The implication is that we can run a simple Markov chain Monte Carlo algorithm and subsequently return the original uncertainty which was removed from the integration. This also has the benefit of avoiding more complicated algorithms which do not perform the integration step. Numerous illustrations are presented.
Source: arXiv (Cornell University)
Authors: Moya, Blake, Walker, Stephen G.
Institution: The University of Texas at Austin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4283208519