So I have a truncated normal in my model. But how do I do a posterior predictive check with such a model? I know there are two ways to force a truncated distribution in the model block, either by parameter bounds or by explicit truncation wtih the T[a,b] modifier. But the explicit truncation approac…

Hi! Would a normal rng with double truncation be correct like this?: real normal_lb_ub_rng(real mu, real sigma, real lb, real ub) { real p1 = normal_cdf(lb, mu, sigma); // cdf with lower bound real p2 = normal_cdf(ub, mu, sigma); // cdf with upper bound real u = uniform_rng(p1, p2); …

RNG for truncated distributions

Modeling

bgoodri January 29, 2018, 4:59pm 6

There is no syntax for it in the Stan language, but you can draw from a uniform distribution and there are some inverse CDFs implemented.

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RNG for truncated distributions

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