Abstract
AbstractWe use Bayesian Estimation for the logistic growth model in order to estimate the spread of the coronavirus epidemic in the state of New York. Models weighting all data points equally as well as models with normal error structure prove inadequate to model the process accurately. On the other hand, a model with larger weights for more recent data points and with t-distributed errors seems reasonably capable of making at least short term predictions.
Publisher
Cold Spring Harbor Laboratory
Reference7 articles.
1. Batista, M. (2020). Estimation of the Final Size of Coronavirus Epidemic by the Logistic Model. medRxiv, 2020.
2. Understanding the Metropolis-Hastings Algorithm
3. Glorot, X. , Bordes, A. , Bengio, Y. (2011). Deep Sparse Rectifier Neural Networks. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, FL, USA, Vol. 15.
4. Bayesian Estimation Supersedes the t test;Journal of Experimental Psychology: General,2012
5. A Flexible Growth Function for Empirical Use
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献