Tau-leaped Particle Learning
DOI:
https://doi.org/10.5210/ojphi.v5i1.4575Abstract
Development of effective policy interventions to stem disease outbreaks requires knowledge of the current state of affairs, e.g. how many individuals are currently infected, a strain's virulence, etc, as well as our uncertainty of these values. A Bayesian inferential approach provides this information, but at a computational expense. We develop a sequential Bayesian approach based on an epidemiological compartment model and noisy count observations of the transitions between compartments.Published
2013-03-24
How to Cite
Niemi, J., & Ludkovski, M. (2013). Tau-leaped Particle Learning. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4575
Issue
Section
Oral Presentations: Analytical Methods - Bayesian