## 21.6 Avoiding Validation

Stan validates all of its data structure constraints. For example, consider a transformed parameter defined to be a covariance matrix and then used as a covariance parameter in the model block.

transformed parameters {
cov_matrix[K] Sigma;
...
}                               // first validation
model {
y ~ multi_normal(mu, Sigma);  // second validation
...

Because Sigma is declared to be a covariance matrix, it will be factored at the end of the transformed parameter block to ensure that it is positive definite. The multivariate normal log density function also validates that Sigma is positive definite. This test is expensive, having cubic run time (i.e., $$\mathcal{O}(N^3)$$ for $$N \times N$$ matrices), so it should not be done twice.

The test may be avoided by simply declaring Sigma to be a simple unconstrained matrix.

transformed parameters {
matrix[K, K] Sigma;
...
model {
y ~ multi_normal(mu, Sigma);  // only validation

Now the only validation is carried out by the multivariate normal.