(ii) Prior distribution
Bayesian approaches incorporate prior knowledge about the parameters
into the model. Our choices for these distributions are summarised in
(Suppl. Table 2) below for each parasite. We choose a common prior for
Sigma, 1/gamma (0.0001,0.0001) and scale parameter (c), gamma (1,10), in
all cases. We selected a flat prior for Tmin and
Tmax within a defined range. We used a gamma
distribution as both the scaling parameter and sigma are non-negative
continuous positive values.
(iii) Likelihood
We choose a normal distribution with mean parameter μ given by the
Briére equation (Briére et al . 1999) as the likelihood of the
data and standard deviation σ. We run STAN
(https://mc-stan.org/) for four
chains of 1000 iterations each, discarding 500 iterations in each case
for warmup. \(\hat{R}\), the convergence statistic reported by STAN, is
close to 1 (< 1.05), indicating the 4 Markov chains are in
close agreement with one another.