This is the Python implemetnation Rajesh et. al's Black Box Variational Inference for a simple 4-d Gaussian model. The code is here more as a proof of concept that this works and contains the AdaGrad extension along with the MCCV needed to control the variance of the estimator. It should be in theory easy to modify this to work with a different model by replacing the gradient of the variational distribution (the Gaussian) with your distribution of choice as well as the log joint probability of the model.
Pandas Numpy Matplotlib Seaborn Scipy
To run simply type:
python bbvi_simple.py
bbvi_gaussian_samples.png - A scatter plot of the first two dimensions for the prior / likelihood / posterior. bbvi_gaussian.png - A facet plot that shows the sample paths for the various implementations of BBVI where the learning rate can be either Robbins-Monroe or AdaGrad and whether Markov Control Variates are used. The diagonal plots represent a histogram of the samples collected from every iteration of BBVI where the yellow represents the true value (made slightly larger for visibility).
