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## 22.1 Multivariate Normal Distribution

### 22.1.1 Probability Density Function

If $$K \in \mathbb{N}$$, $$\mu \in \mathbb{R}^K$$, and $$\Sigma \in \mathbb{R}^{K \times K}$$ is symmetric and positive definite, then for $$y \in \mathbb{R}^K$$, $\text{MultiNormal}(y|\mu,\Sigma) = \frac{1}{\left( 2 \pi \right)^{K/2}} \ \frac{1}{\sqrt{|\Sigma|}} \ \exp \! \left( \! - \frac{1}{2} (y - \mu)^{\top} \, \Sigma^{-1} \, (y - \mu) \right) \! ,$ where $$|\Sigma|$$ is the absolute determinant of $$\Sigma$$.

### 22.1.2 Sampling Statement

y ~ multi_normal(mu, Sigma)

Increment target log probability density with multi_normal_lupdf(y | mu, Sigma).

### 22.1.3 Stan Functions

The multivariate normal probability function is overloaded to allow the variate vector $$y$$ and location vector $$\mu$$ to be vectors or row vectors (or to mix the two types). The density function is also vectorized, so it allows arrays of row vectors or vectors as arguments; see section vectorized function signatures for a description of vectorization.

real multi_normal_lpdf(vectors y | vectors mu, matrix Sigma)
The log of the multivariate normal density of vector(s) y given location vector(s) mu and covariance matrix Sigma

real multi_normal_lupdf(vectors y | vectors mu, matrix Sigma)
The log of the multivariate normal density of vector(s) y given location vector(s) mu and covariance matrix Sigma dropping constant additive terms

real multi_normal_lpdf(vectors y | row_vectors mu, matrix Sigma)
The log of the multivariate normal density of vector(s) y given location row vector(s) mu and covariance matrix Sigma

real multi_normal_lupdf(vectors y | row_vectors mu, matrix Sigma)
The log of the multivariate normal density of vector(s) y given location row vector(s) mu and covariance matrix Sigma dropping constant additive terms

real multi_normal_lpdf(row_vectors y | vectors mu, matrix Sigma)
The log of the multivariate normal density of row vector(s) y given location vector(s) mu and covariance matrix Sigma

real multi_normal_lupdf(row_vectors y | vectors mu, matrix Sigma)
The log of the multivariate normal density of row vector(s) y given location vector(s) mu and covariance matrix Sigma dropping constant additive terms

real multi_normal_lpdf(row_vectors y | row_vectors mu, matrix Sigma)
The log of the multivariate normal density of row vector(s) y given location row vector(s) mu and covariance matrix Sigma

real multi_normal_lupdf(row_vectors y | row_vectors mu, matrix Sigma)
The log of the multivariate normal density of row vector(s) y given location row vector(s) mu and covariance matrix Sigma dropping constant additive terms

Although there is a direct multi-normal RNG function, if more than one result is required, it’s much more efficient to Cholesky factor the covariance matrix and call multi_normal_cholesky_rng; see section multi-variate normal, cholesky parameterization.

vector multi_normal_rng(vector mu, matrix Sigma)
Generate a multivariate normal variate with location mu and covariance matrix Sigma; may only be used in transformed data and generated quantities blocks

vector multi_normal_rng(row_vector mu, matrix Sigma)
Generate a multivariate normal variate with location mu and covariance matrix Sigma; may only be used in transformed data and generated quantities blocks

vectors multi_normal_rng(vectors mu, matrix Sigma)
Generate an array of multivariate normal variates with locations mu and covariance matrix Sigma; may only be used in transformed data and generated quantities blocks

vectors multi_normal_rng(row_vectors mu, matrix Sigma)
Generate an array of multivariate normal variates with locations mu and covariance matrix Sigma; may only be used in transformed data and generated quantities blocks