This repo contains different variational methods to learn a Gaussian Mixture Model (GMM) and an Univariate Gaussian (UGM) from data. It also contains documentation regarding the algorithm derivations, 2D interpolation scripts, dimensionality reduction scripts, map visualization scripts and other interesting models.

• inference/python/ugm_cavi.py Coordinate Ascent Variational Inference (CAVI) algorithm to learn an UGM.

• inference/python/gmm_means_cavi.py Coordinate Ascent Variational Inference (CAVI) algorithm to learn a GMM with unknown means but known precisions.
• inference/python/gmm_cavi.py Coordinate Ascent Variational Inference (CAVI) algorithm to learn a GMM with unknown means and unknown precisions.
• inference/python/gmm_scavi.py Sthocastic Coordinate Ascent Variational Inference (SCAVI) algorithm to learn a GMM with unknown means and unknown precisions.

• inference/tensorflow/ugm_cavi.py Coordinate Ascent Variational Inference (CAVI) algorithm to learn an UGM.
• inference/tensorflow/ugm_cavi_linesearch.py Coordinate Ascent Variational Inference (CAVI) with linesearch algorithm to learn an UGM.
• inference/tensorflow/ugm_gavi.py contains a Gradient Ascent Variational Inference (GAVI) algorithm to learn an UGM.

• inference/tensorflow/gmm_means_cavi.py Coordinate Ascent Variational Inference (CAVI) algorithm to learn a GMM with unknown means but known precisions.
• tinference/tensorflow/gmm_means_cavi_linesearch.py Coordinate Ascent Variational Inference (CAVI) with linesearch algorithm to learn a GMM with unknown means but known precisions.
• inference/tensorflow/gmm_means_gavi.py Gradient Ascent Variational Inference (GAVI) algorithm to learn a GMM with unknown means but known precisions.
• ìnference/tensorflow/gmm_gavi.py Gradient Ascent Variational Inference (GAVI) algorithm to learn a GMM with unknown means and unknown precisions.
• inference/tensorflow/gmm_sgavi.py Sthocastic Gradient Ascent Variational Inference (SGAVI) algorithm to learn a GMM with unknown means and unknown precisions.

• inference/autograd/gmm_means_cavi.py Coordinate Ascent Variational Inferecne (CAVI) algorithm to learn a GMM with unknown means but known precisions.
• [IN PROCESS] inference/autograd/gmm_means.py Coordinate Ascent Variational Inference (CAVI) algorithm to learn a GMM with unknown means and unknown precisions.

• [IN PROCESS] inference/edward/ugm_bbvi.py Black Box Variational Inference (BBVI) algorithm to learn an UGM.

• [IN PROCESS] inference/edward/gmm_bbvi.py Black Box Variational Inference (BBVI) algorithm to learn a GMM with unknown means and unknown precisions.

• data/synthetic/synthetic_data_generator.py generates data from a mixture of gaussians with different precision matrix per each components.
• data/synthetic/synthetic_data_generator_means.py generates data from a mixture of gaussians with a given precision matrix for all components.

• preprocessing/interpolation/nn_interpolation.py Nearest Neighbors interpolation.
• preprocessing/interpolation/linear_interpolation.R Linear interpolation.

• preprocessing/maps/map.R R map
• preprocessing/maps/gmap.R R Google map

• preprocessing/dimReduction/pca.py Sklearn Principal Component Analysis.
• preprocessing/dimReduction/ipca.py Sklearn Incremental Principal Component Analysis.
• preprocessing/dimReduction/ae.py Keras autoencoder.
• preprocessing/dimReduction/ppca.py Edward Probabilistic Principal Component Analysis.

• models/dirichlet_categorical.py Exact inference in a Dirichlet Categorical model.
• models/invgamma_normal.py Exact inference in a Inverse-Gamma Normal model.
• models/NIW_normal.py Exact inference in a Normal-Inverse-Wishart Normal model.

• models/linear_regression_tf.py Linear regression model optimization with Gradient Descent algorithm.

• models/linear_regression_ag.py Linear regression model optimization with Gradient Descent algorithm.

• models/dirichlet_categorical_edward.py Black Box Variational Inference in a Dirichlet Categorical model.
• models/invgamma_normal_edward.py Black Box Variational Inference in a Inverse-Gamma Normal model.
• [IN PROCESS] models/NW_normal_edward.py Black Box Variational Inference in a Normal-Wishart Normal model.

• docs/ contains the GMM derivation of CAVI algorithm

• Set environment variables $PROJECT and $WORKON_HOME in setup/provision.sh
• Execute setup/provision.sh