The ppca packages implements different inference methods for Probabilistic Principal Component Analysis described by Christopher Bishop.
Python implementation followed the way from the book A First Course in Machine Learning by Simon Rogers and Mark Girolami from Chapter 7.5 to 7.7
ppca.py: probabilistic PCA for continuous values (Simon's book Chapter 7.5), update tau, X and W when doing EM.
probit_ppca.py: probit ppca for binary values (Simon's book Chapter 7.7), apply probit function, update Q, bias, X and W when doing EM.
Also, borrowed some code from: https://github.com/cangermueller/ppca