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jgabry merged 1 commit into from
Feb 11, 2026
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Use consistent syntax in profiling vignette #1137

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26 changes: 13 additions & 13 deletions vignettes/profiling.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ However, be aware that the statistical assumptions that go into a model are
the most important factors in overall model performance. It is often not
possible to make up for model problems with just brute force computation. For
ideas on how to address performance of your model from a statistical
perspective, see Gelman (2020).
perspective, see Gelman et al. (2020).

```{r library, message=FALSE}
library(cmdstanr)
Expand Down Expand Up @@ -66,11 +66,11 @@ calculations with `profile` statements.

```
profile("priors") {
target += std_normal_lpdf(beta);
target += std_normal_lpdf(alpha);
beta ~ std_normal();
alpha ~ std_normal();
}
profile("likelihood") {
target += bernoulli_logit_lpmf(y | X * beta + alpha);
y ~ bernoulli_logit(X * beta + alpha);
}
```

Expand All @@ -92,11 +92,11 @@ parameters {
}
model {
profile("priors") {
target += std_normal_lpdf(beta);
target += std_normal_lpdf(alpha);
beta ~ std_normal();
alpha ~ std_normal();
}
profile("likelihood") {
target += bernoulli_logit_lpmf(y | X * beta + alpha);
y ~ bernoulli_logit(X * beta + alpha);
}
}
')
Expand Down Expand Up @@ -145,7 +145,7 @@ Stan's specialized glm functions can be used to make models like this faster. In
this case the likelihood can be replaced with

```
target += bernoulli_logit_glm_lpmf(y | X, alpha, beta);
y ~ bernoulli_logit_glm(X, alpha, beta);
```

We'll keep the same `profile()` statements so that the profiling information for
Expand All @@ -165,11 +165,11 @@ parameters {
}
model {
profile("priors") {
target += std_normal_lpdf(beta);
target += std_normal_lpdf(alpha);
beta ~ std_normal();
alpha ~ std_normal();
}
profile("likelihood") {
target += bernoulli_logit_glm_lpmf(y | X, alpha, beta);
y ~ bernoulli_logit_glm(X, alpha, beta);
}
}
')
Expand All @@ -184,8 +184,8 @@ fit_glm <- model_glm$sample(data = stan_data, chains = 1)
fit_glm$profiles()
```

We can see from the `total_time` column that this is much faster than the
previous model.
We can see from the `total_time` column that the likelihood computation is
faster than in the previous model.

## Per-gradient timings, and memory usage

Expand Down
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