# 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]1:

   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:

   add[1,~exponential[10]]

In these cases, we use "=" to set the parameter value:
   rs07[mean_length=add[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]]
