A Bayesian Logistic Growth Model for the Spread of COVID-19 in New York

Svetoslav Bliznashki

As a whole it appears that the combination of Bayesian Estimation, differentially weighing the observations, and employing a more robust approach towards modeling the %%%%%%%%%%%%%%%Proposal Covariance Matrix%%%%%%%%%%%%%%%% %should be imported as covmat to Matlab before running the above program; %%%%%Function calculating the density for the Generalized t-distribution%%%%%%%% %the function is used to calculate the likelihood; function y = gentdst(x, m, s, v) %x -data point, m -location, s -scale, v 0 degrees of freedom; c=(1/sqrt(v))*(1/(beta(v/2, 0.5))); y=(c/s)*(1+((x-m).^2)/(v*(s^2))).^(-0.5*(v+1));