Hi all,

My model currently contains ~1000 variables

**y ~ normal ( beta1 x1[n] + beta2x2[n] + … , sigma)**

with some terms defined as

**beta * x ^ parameter**

**beta * 1 / (1 + exp(-0.05 * ((x - parameter1)) / parameter2)) - (1 / (1 + exp(-0.05 * (( 0 - parameter1)) / parameter2)))**

From the manual it says that vectorized form is faster for Rstan, which prompted me to converting my program and data. Would it certainly be faster for this case where there would be several terms and parameters? I tried running it unvectorized and the model took too long to setup.

Another concern would be constraining beta vectors element-wise. Since rstan can only define 1 set of bounds for a vector, is this a valid work-around where we retain beta as scalar under parameters and under model define a vector which will group a set of beta of the predictors (got the idea from here)?

i.e.

parameters{

real <lower=0.5, upper=1.5> beta1

real <lower=0.2, upper=2.5> beta2

…

}

model{

beta1 ~ normal (mean, sd)

beta2 ~ normal (mean, sd)

…

vector [K] beta_vector = [beta1, beta2, …]’

y ~ normal(…)

}

Using rstan 2.17.3 a Windows 7 with 8GB ram. Would be glad for any help/clarification. Thanks!