Hello all, I have a question , suppose I am running the following regression: data { vector[N] Y; vector[N] X; } parameters { real alpha; real<lower=0> sigma_y; real beta; vector<lower=0>[N] u; real<lower=0> sigma_u; } model { alpha ~ normal(0,2); sigma_y ~ inv_gamma(1,1); sigma_u ~…

[image] ants007: I am wondering if I declare the vector u to be vector<lower=0>[N] u, then is there any point in adding T[0,]? The truncation is redundant to the lower bound in the declaration, and most folks use just the latter (I think bc that causes Stan to make a hidden unbounded paramete…

Thanks Mike once again. So T[0,] is the recommended option from both the accuracy and efficiency point of view?

No, setting the bound in the parameter-block declaration is the recommended approach. Sorry, I belatedly realize my use of “latter” was confusing given I used a different order in referring to the two approaches than you did.

Thank you Mike for clarifying. This real helps. Thanks once again! Antony

Sampling from truncated normal distribution

Modeling

ants007 October 6, 2021, 9:00am 2

Can comebody please help here!

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Sampling from truncated normal distribution

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