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!

Simulating fake data for regression in Stan

Topic		Replies	Views
Effect of declaring truncated distributions for parameters which already have a range declared Modeling specification	3	744	April 26, 2022
Non-centered parametrisation of truncated normal distribution Modeling techniques , fitting-issues , specification	8	1096	July 6, 2022
Sampling from a user-defined truncated distribution Modeling fitting-issues , specification	4	665	October 6, 2021
Understanding truncation in Stan Modeling specification	3	1342	September 2, 2019
Reparameterizing a Truncated Normal Modeling	16	3770	July 19, 2019

Sampling from truncated normal distribution

Related topics