`joint`

combines univariate probability distributions
together into a multivariate (and *a priori* independent between
dimensions) joint distribution, either over a variable, or for fixed data.

joint(..., dim = NULL)

## Arguments

... |
scalar variable greta arrays following probability distributions
(see `distributions()` ); the components of the joint
distribution. |

dim |
the dimensions of the greta array to be returned, either a scalar
or a vector of positive integers. The final dimension of the greta array
returned will be determined by the number of component distributions |

## Details

The component probability distributions must all be either
continuous or discrete, and must have the same dimensions.

This functionality is unlikely to be useful in most models, since the same
result can usually be achieved by combining variables with separate
distributions. It is included for situations where it is more convenient to
consider these as a single distribution, e.g. for use with
`distribution`

or `mixture`

.

## Examples

# NOT RUN {
# an uncorrelated bivariate normal
x <- joint(normal(-3, 0.5), normal(3, 0.5))
m <- model(x)
plot(mcmc(m, n_samples = 500))
# joint distributions can be used to define densities over data
x <- cbind(rnorm(10, 2, 0.5), rbeta(10, 3, 3))
mu <- normal(0, 10)
sd <- normal(0, 3, truncation = c(0, Inf))
a <- normal(0, 3, truncation = c(0, Inf))
b <- normal(0, 3, truncation = c(0, Inf))
distribution(x) <- joint(normal(mu, sd), beta(a, b),
dim = 10
)
m <- model(mu, sd, a, b)
plot(mcmc(m))
# }