Make Baysian approach vs. maximum likelihood explicit

Author: krasserm

latent_variable_models_part_1_pymc3.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "## Gaussian mixture models with PyMC3\n",
     "\n",
-    "This is a PyMC3 implementation of the example in [Latent variable models - part 1: Gaussian mixture models and the EM algorithm](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/latent_variable_models_part_1.ipynb). The code is mainly inspired by [this notebook](https://github.com/junpenglao/Planet_Sakaar_Data_Science/blob/master/WIP/%5BWIP%5D%20Bayesian%20GMM.ipynb)."
+    "This is a PyMC3 implementation of the example in [Latent variable models - part 1: Gaussian mixture models and the EM algorithm](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/latent_variable_models_part_1.ipynb). In contrast to the linked article, model parameters are inferred using a Bayesian approach insetad of maximum likelihood. The following code is mainly inspired by [this notebook](https://github.com/junpenglao/Planet_Sakaar_Data_Science/blob/master/WIP/%5BWIP%5D%20Bayesian%20GMM.ipynb)."
    ]
   },
   {