# 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(LogNormal(1,1)))

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

   HKY85(kappa=~LogNormal(1,1))

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

   HKY85(kappa~LogNormal(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))
