The goal of this experiment is to understand two things:
a) How do the priors on gamma and beta affect inferences for R0?
To answer a) we could:
Run the two ODE model parametrised in terms of gamma and beta:
Informative log-normal priors;
"Uninformative" Gamma(1, 1) priors;
"Uninformative" half-normal(0, 1) priors;
"Uninformative" log-normal priors: mu = 0 and sd =1, 10, 100
Run the two ODE model parametrised in terms of gamma and R0 with
Informative Gamma priors on both gamma and R0;
Informative half-normal priors on both gamma and R0;
Informative log-normal priors on both gamma and R0;
An informative Gamma prior on R_0 and an uninformative Gamma prior on gamma.
An informative Gamma prior on R_0 and an uninformative half-normal prior on gamma.
An informative log-normal prior on R_0 and an uninformative log-normal prior on gamma.
An informative log-normal prior on R_0truncated at 1 (epidemic prior) and an uninformative prior on gamma.
Quantities to track
mean, median and BCI;
Divergences;
ESS;
Run time.
Q: Why use different prior families? A: to assess tail effects.
Obs: we should try to make all of the "informative" priors be moment-matching. E.g.: if we pick a Gamma(2, 1) prior for R0, then the informative half-normal prior would have to be a half-normal(m0, s0), with m0and s0 chosen so as to have the same first two moments.
The goal of this experiment is to understand two things:
gammaandbetaaffect inferences for R0?To answer a) we could:
Run the two ODE model parametrised in terms of
gammaandbeta:Informative log-normal priors;
"Uninformative" Gamma(1, 1) priors;
"Uninformative" half-normal(0, 1) priors;
"Uninformative" log-normal priors:
mu = 0andsd =1, 10, 100Run the two ODE model parametrised in terms of
gammaandR0withInformative Gamma priors on both
gammaandR0;Informative half-normal priors on both
gammaandR0;Informative log-normal priors on both
gammaandR0;An informative Gamma prior on
R_0and an uninformative Gamma prior ongamma.An informative Gamma prior on
R_0and an uninformative half-normal prior ongamma.An informative log-normal prior on
R_0and an uninformative log-normal prior ongamma.An informative log-normal prior on
R_0truncated at 1 (epidemic prior) and an uninformative prior ongamma.Quantities to track
Q: Why use different prior families? A: to assess tail effects. Obs: we should try to make all of the "informative" priors be moment-matching. E.g.: if we pick a Gamma(2, 1) prior for
R0, then the informative half-normal prior would have to be a half-normal(m0, s0), withm0ands0chosen so as to have the same first two moments.