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. 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))
# }