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

@@ -123,16 +123,17 @@ class GaussianLikelihood(Likelihood):
         if samplername != 'pymc3':
             raise ValueError("Only use this class method for PyMC3 sampler")
 
-        model_parameters = dict()
-        for key in sampler.priors:
-            if key == 'sigma' and self.sigma is None:
-                self.sigma = sampler.priors[key].pymc3_prior(sampler)
+        if 'sigma' in sampler.pymc3_priors:
+            # if sigma is suppled use that value
+            if self.sigma is None:
+                self.sigma = sampler.pymc3_priors.pop('sigma')
             else:
-                if key in self.function_keys:
-                    model_parameters[key] = sampler.priors[key].pymc3_prior(sampler)
-                else:
-                    raise ValueError("Prior key '{}' is not a function key!".format(key))
+                del sampler.pymc3_priors['sigma']
 
-        model = self.function(self.x, **model_parameters)
+        for key in sampler.pymc3_priors:
+            if key not in self.function_keys:
+                raise ValueError("Prior key '{}' is not a function key!".format(key))
+
+        model = self.function(self.x, **sampler.pymc3_priors)
 
         return sampler.external_sampler.Normal('likelihood', mu=model, sd=self.sigma, observed=self.y)
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