symbolic-pymc

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~~In RandomVariable.make_node, return theano.gof.Apply(self, inputs, (rng.type(), out_var, log_lik))—where log_lik is a graph of the log-likelihood for the given RV. This addition will allow RandomVariables to represent both measure and sample-space graphs.~~

~~In this case, a random variable's—e.g. rv—complete log-likelihood would always be available as rv.owner.outputs[-1].~~

Since owner information needs to be attached to Op outputs, we can't take that approach (e.g. some log-likelihoods may be constants). Instead, we should simply provide a logp function that constructs the measure-space graph for a given RandomVariable output using its RandomVariable.logp implementation.