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May 30, 2017, 11:10:12 AM5/30/17

to Stan users mailing list

Can anyone of you help me out diagnosing the problem with this simple example model and maybe give some on advice on how to fix it ?

It gives me a bunch of divergent transitions. Funnel like correlations and a highly curved posterior are apparent but I don't really get where those correlations come from.

Maybe I can implement the noncentered paramterization in a different way?

Thanks in advance,

Jökull

nonCentered <- "

data {

int<lower=1> Ntotal;

vector[Ntotal] pred;

vector[Ntotal] pred2;

vector[Ntotal] y;

}

parameters {

real<lower=0.0> sigma;

real<lower=0.0> sigma_w;

real<lower=0.0> sigma_w2;

real mu;

real w_raw;

real w_raw2;

}

transformed parameters {

real w;

real w2;

vector[Ntotal] y_mu;

for (n in 1:Ntotal) y_mu[n] = 0.0;

w = sigma_w * w_raw + mu;

w2 = sigma_w2 * w_raw2 + mu;

y_mu = w * pred + w2 * pred2;

}

model {

sigma_w ~ normal(1,1);

sigma_w2 ~ normal(1,1);

sigma ~ normal(1,1);

mu ~ normal(1,0.2);

w_raw ~ normal(0, 1);

w_raw2 ~ normal(0, 1);

y ~ normal(y_mu,sigma);

}

"

fit <-

stan(

model_code = nonCentered,

data = list(pred = c(0,2,0,0,2,0,0,2,0),

pred2 = c(1,0,1,1,0,1,1,0,1),

Ntotal =9, y = c(3,2,3,3,2,3,3,2,3)),

model_name = 'example',

iter = 1200,

chains = 2

)

May 30, 2017, 4:12:59 PM5/30/17

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Hi, just a quick comment, you can just say 0, you don't need to say 0.0. Also, this line:

for (n in 1:Ntotal) y_mu[n] = 0.0;

can become simply this:

y_mu = 0;

This won't solve your problem but it should make the model easier to read.

A

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May 30, 2017, 6:47:07 PM5/30/17

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> On May 30, 2017, at 4:12 PM, Andrew Gelman <gel...@stat.columbia.edu> wrote:

>

> Hi, just a quick comment, you can just say 0, you don't need to say 0.0. Also, this line:

> for (n in 1:Ntotal) y_mu[n] = 0.0;

> can become simply this:

> y_mu = 0;

> This won't solve your problem but it should make the model easier to read.

y_mu = rep_vector(0, Ntotal);

As to the bigger problem, how much data do you have? The centered

parameterization is better when there's more data.

- Bob

May 31, 2017, 5:11:18 AM5/31/17

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Hey thanks for the quick replies and comments!

I posted the data; it's 9 data point.

May 31, 2017, 4:22:06 PM5/31/17

to stan-...@googlegroups.com

Unless those 9 data points have very low variance, you're

probably going to be better off with the non-centered

parameterization.

- Bob

probably going to be better off with the non-centered

parameterization.

- Bob

Jun 2, 2017, 5:26:06 AM6/2/17

to Stan users mailing list

I think a lot of your troubles come from the fact that your predictors have a perfect linear relationship with y. y = 3 - .5 * pred or y = 2 + pred2. If you would have an intercept in your model and you would be modeling y ~ normal(a0 + a1 pred + a2 pred2, sigma) than the nuts sampler would have to switch between the equally probably modes (a0 = 3, a1 = -.5, a2 = 0) and (a0 = 2, a1 = 0, a2 = 1). This is likely to lead to divergences and weird curvature.

If I read your model correctly, there is no intercept a0 in your model but I still think that the linear relations in your data (see also pred = 2 - 2*pred2) are causing havoc.

Stijn

Jun 4, 2017, 8:33:07 PM6/4/17

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Theres a discussion of just this issue in the problematic posteriors

chapter of the manual. It can be partially tamed with priors, but

it's usually much better to just identify the model if you know there's

a problem.

- Bob

chapter of the manual. It can be partially tamed with priors, but

it's usually much better to just identify the model if you know there's

a problem.

- Bob

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