# Vectorizing bernoulli\_logit\_lpmf()

**URL:** <https://discourse.mc-stan.org/t/vectorizing-bernoulli-logit-lpmf/1218>\
**Category:** Algorithms\
**Created:** [July 12, 2017, 2:51pm UTC](https://discourse.mc-stan.org/t/vectorizing-bernoulli-logit-lpmf/1218 "2017-07-12T14:51:00Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![fusaroli](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/fusaroli/32/477_2.png) [@fusaroli](https://discourse.mc-stan.org/u/fusaroli)\
**Post date:** [July 12, 2017, 2:51pm UTC](https://discourse.mc-stan.org/t/vectorizing-bernoulli-logit-lpmf/1218/1 "2017-07-12T14:51:00Z")

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I’ve been looking into vectorizing the code for a Stan model and stumbled upon the issue of not being able to vectorizing bernoulli\_logit\_lpmf()

the current (working) code says

for (i in 1:N) {  
log\_lik[i] = bernoulli\_logit\_lpmf( Outcome[i] | mu[i] )  
}

I’d like to turn it into

log\_lik = bernoulli\_logit\_lpmf( Outcome | mu)

but this does not seem to work. Is there something obvious I’m missing, or is the function not vectorizable?

Thanks.  
Riccardo

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**Author:** ![bgoodri](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bgoodri/32/4451_2.png) [@bgoodri](https://discourse.mc-stan.org/u/bgoodri)\
**Post date:** [July 12, 2017, 3:00pm UTC](https://discourse.mc-stan.org/t/vectorizing-bernoulli-logit-lpmf/1218/2 "2017-07-12T15:00:40Z")

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If you are trying to obtain the log likelihood for each observation, then the loop version is the only way to do it. The `_lp{m,d}f` functions return a scalar when passed containers, representing the log likelihood summed over conditionally independent observations.

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**Author:** ![fusaroli](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/fusaroli/32/477_2.png) [@fusaroli](https://discourse.mc-stan.org/u/fusaroli)\
**Post date:** [July 12, 2017, 3:06pm UTC](https://discourse.mc-stan.org/t/vectorizing-bernoulli-logit-lpmf/1218/3 "2017-07-12T15:06:09Z")

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ok. thanks! this makes sense.