Selected article for: "accurate estimation and logistic growth"
Author: Svetoslav Bliznashki
Title: A Bayesian Logistic Growth Model for the Spread of COVID-19 in New York
  • Document date: 2020_4_7
    • ID: lhv83zac_34
    Snippet: Discussion. It appears that a logistic growth model with a weighted likelihood function and a t-distribution imposed on the error structure is able to make accurate short term predictions of the spread of a disease. The Bayesian estimation gives more accurate estimates than traditional Least Squares and Maximum Likelihood approaches with more accurate interval estimates. Moreover, the Bayesian posteriors (including the predictive distributions) h.....
    Document: Discussion. It appears that a logistic growth model with a weighted likelihood function and a t-distribution imposed on the error structure is able to make accurate short term predictions of the spread of a disease. The Bayesian estimation gives more accurate estimates than traditional Least Squares and Maximum Likelihood approaches with more accurate interval estimates. Moreover, the Bayesian posteriors (including the predictive distributions) have a straightforward probabilistic interpretation which cannot be said about traditional frequentist Confidence Intervals.

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