`distribution`

defines probability distributions over
observed data, e.g. to set a model likelihood.

distribution(greta_array) <- value
distribution(greta_array)

## Arguments

greta_array |
a data greta array. For the assignment method it must not
already have a probability distribution assigned |

value |
a greta array with a distribution (see
`distributions()` ) |

## Details

The extract method returns the greta array if it has a distribution,
or `NULL`

if it doesn't. It has no real use-case, but is included for
completeness

## Examples

# NOT RUN {
# define a model likelihood
# observed data and mean parameter to be estimated
# (explicitly coerce data to a greta array so we can refer to it later)
y <- as_data(rnorm(5, 0, 3))
mu <- uniform(-3, 3)
# define the distribution over y (the model likelihood)
distribution(y) <- normal(mu, 1)
# get the distribution over y
distribution(y)
# }