Dear paul,

Thank you for showing me how to formulate the non-linear model and set the bounded priors.

Nevertheless, I have a follow up question, and I was not sure if I needed to make a new post?

In fact, my parameters are assumed to depend on varios physical and chemical parameters.

How do I set priors on both the Intercept and the predictors? For example,

bform <- bf(firmness ~ (minutes > tg) * Gm * (1 - exp(-k * (minutes - tg))),

tg ~ pH + dose,

Gm ~ pH + protein,

k ~ pH + temperature,

nl = TRUE)

Would it then be correct to set priors as follows:

bprior <- prior(normal(11, 1), nlpar = tg, coef = Intercept)+

prior(normal(0, 10), nlpar = tg, coef = pH)+

prior(normal(0, 10), nlpar = tg, coef = dose)+

prior(normal(260, 1), nlpar = Gm, coef = Intercept)+

prior(normal(0, 10), nlpar = Gm, coef = pH)+

prior(normal(0, 10), nlpar = Gm, coef = protein)+

prior(normal(0.23, 0.05), nlpar = k, coef = Intercept)+

prior(normal(0, 1), nlpar = k, coef = pH)

prior(normal(0, 1), nlpar = k, coef = Temperature)

Or is there a better way?

/Jannik