Add PyMC3 sampler

I have started an attempt to add PyMC3 as a sampler option.

At the moment this can be used with the Uniform prior, for which I have added a new pymc3 method, and the GaussianLikelihood for which I have added a pymc3_likelihood method. I can convert the other prior classes to also have pymc3 methods too.

At the moment I've just got PyMC3 to run with the default NUTS sampler, but can change the _run_external_sampler method to take in other allowed sampler keyword args.

I've tested this with a linear regression example and it works!

tupak/core/likelihood.py

@@ -236,3 +236,27 @@ class PoissonLikelihood(Likelihood):
                         -self.sumlogfactorial)
         else:
             raise ValueError("Poisson rate function returns wrong value type!")
+
+    def pymc3_likelihood(self, sampler):
+        from tupak.core.sampler import Sampler
+
+        if not isinstance(sampler, Sampler):
+            raise ValueError("'sampler' is not a Sampler class")
+
+        try:
+            samplername = sampler.external_sampler.__name__
+        except ValueError:
+            raise ValueError("Sampler's 'external_sampler' has not been initialised")
+
+        if samplername != 'pymc3':
+            raise ValueError("Only use this class method for PyMC3 sampler")
+
+        for key in sampler.pymc3_priors:
+            if key not in self.function_keys:
+                raise ValueError("Prior key '{}' is not a function key!".format(key))
+
+        # get rate function
+        rate = self.function(self.x, **sampler.pymc3_priors)
+
+        return sampler.external_sampler.Poisson('likelihood', mu=rate,
+                                                observed=self.y)