Computes the Gibbs transition models from a Bayesian Network.
        'Probabilistic Graphical Model Principles and Techniques', Koller and
        Friedman, Section 12.3.3 pp 512-513.

        Parameters:
        -----------
        model: BayesianModel
            The model from which probabilities will be computed.

def _get_kernel_from_bayesian_model(self, model):
        """
        Computes the Gibbs transition models from a Bayesian Network.
        'Probabilistic Graphical Model Principles and Techniques', Koller and
        Friedman, Section 12.3.3 pp 512-513.

        Parameters:
        -----------
        model: BayesianModel
            The model from which probabilities will be computed.
        """
        self.variables = np.array(model.nodes())
        self.cardinalities = {var: model.get_cpds(var).variable_card for var in self.variables}

        for var in self.variables:
            other_vars = [v for v in self.variables if var != v]
            other_cards = [self.cardinalities[v] for v in other_vars]
            cpds = [cpd for cpd in model.cpds if var in cpd.scope()]
            prod_cpd = factor_product(*cpds)
            kernel = {}
            scope = set(prod_cpd.scope())
            for tup in itertools.product(*[range(card) for card in other_cards]):
                states = [State(v, s) for v, s in zip(other_vars, tup) if v in scope]
                prod_cpd_reduced = prod_cpd.reduce(states, inplace=False)
                kernel[tup] = prod_cpd_reduced.values / sum(prod_cpd_reduced.values)
            self.transition_models[var] = kernel