fixed broken URLs

docs/BayesianNetwork.rst

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 Bayesian Networks
 =================
 
-- `IPython Notebook Tutorial <https://github.com/jmschrei/pomegranate/blob/master/tutorials/Tutorial_4_Bayesian_Networks.ipynb>`_
-- `IPython Notebook Structure Learning Tutorial <https://github.com/jmschrei/pomegranate/blob/master/tutorials/Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb>`_
+- `IPython Notebook Tutorial <https://github.com/jmschrei/pomegranate/blob/1b4b756472b8d19fde6bf4c8cc85da9a8706b3e0/tutorials/Tutorial_4_Bayesian_Networks.ipynb>`_
+- `IPython Notebook Structure Learning Tutorial <https://github.com/jmschrei/pomegranate/blob/1b4b756472b8d19fde6bf4c8cc85da9a8706b3e0/tutorials/Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb>`_
 
 `Bayesian networks <http://en.wikipedia.org/wiki/Bayesian_network>`_ are a probabilistic model that are especially good at inference given incomplete data. Much like a hidden Markov model, they consist of a directed graphical model (though Bayesian networks must also be acyclic) and a set of probability distributions. The edges encode dependency statements between the variables, where the lack of an edge between any pair of variables indicates a conditional independence. Each node encodes a probability distribution, where root nodes encode univariate probability distributions and inner/leaf nodes encode conditional probability distributions. Bayesian networks are exceptionally flexible when doing inference, as any subset of variables can be observed, and inference done over all other variables, without needing to define these groups in advance. In fact, the set of observed variables can change from one sample to the next without needing to modify the underlying algorithm at all.