Hello Everyone,

I’m new to Stan. I’m trying to do some Bayesian analysis on data that follows a Weibull distribution in RStan. I’d like to obtain a posterior distribution for the two parameters (shape and scale) and then understand the full predictive distribution of the data.

The code I have now is pasted below. Can anyone help refine the code to meet my goals? Thank you.

my_weibull_code ← "

data {

int<lower=0> N; // number of observations

vector [N] my_data; // observed data

}

parameters {

real<lower=0> lambda; // scale

real<lower=0> k; // shape

}

model {

// prior

target + = -log(lambda) // Un-informative Jeffrey’s prior, open to suggestions here

k ~ normal(0,3) // Weakly informative

// model

my_data ~ weibull (lambda, k);

}

"

my_weibull ← stan_model(model_code = my_weibull_code)