# Specifying priors:

Priors on model parameters are specified by giving a random value.
Random values can be obtained from distributions using the function
`sample`.  For example, this places a log-normal prior on kappa:

   hky85(kappa=sample(log_normal(1,1)))

You can write `~Dist` as a shorthand for `sample(Dist)`:

   hky85(kappa=~log_normal(1,1))

The `=~` can be further shortened to just `~`:

   hky85(kappa~log_normal(1,1))

It also is possible to use random values as inputs to other functions:

   1 + ~exponential(10)

In these cases, we use "=" to set the parameter value:
   rs07(mean_length=1+~exponential(10))

# Random values vs Distributions

Random values and distributions are of different types.  Thus the
following is of type `Distribution<Double>`:

   uniform(0,1)

In contrast, the following are both of type `Double`:

   sample(uniform(0,1))
   ~uniform(0,1)

This is important when passing distributions as arguments to other
distributions and functions.  Thus, this works:

   iid(4,normal(0,1))

But, this is incorrect:

   iid(4,~normal(0,1))
