Initial refactoring for RandomVariable use in Model, step methods, and basic dists

These changes can be summarized as follows:
- `Model` objects now track fully functional Theano graphs that represent all
relationships between random and "deterministic" variables.  These graphs are
called these "sample-space" graphs.  `Model.unobserved_RVs`, `Model.basic_RVs`,
`Model.free_RVs`, and `Model.observed_RVs` contain these
graphs (i.e. `TensorVariable`s), which are generated by `RandomVariable` `Op`s.
- For each random variable, there is now a corresponding "measure-space"
variable (i.e. a `TensorVariable` that corresponds to said variable in a
log-likelihood graph).  These variables are available as `rv_var.tag.value_var`,
for each random variable `rv_var`, or via `Model.vars`.
- Log-likelihood (i.e. measure-space) graphs are now created for individual
random variables by way of the generic functions `logpt`, `logcdf`,
`logp_nojac`, and `logpt_sum` in `pymc3.distributions`.
- Numerous uses of concrete shape information stemming from `Model`
objects (e.g. `Model.size`) have been removed/refactored.
- Use of `FreeRV`, `ObservedRV`, `MultiObservedRV`, and `TransformedRV` has been
deprecated.  The information previously stored in these classes is now tracked
using `TensorVariable.tag`, and log-likelihoods are generated using the
aforementioned `log*` generic functions.

Author: brandonwillard