  This paper deals with estimating model parameters in graphical models. We
reformulate it as an information geometric optimization problem and introduce a
natural gradient descent strategy that incorporates additional meta parameters.
We show that our approach is a strong alternative to the celebrated EM approach
for learning in graphical models. Actually, our natural gradient based strategy
leads to learning optimal parameters for the final objective function without
artificially trying to fit a distribution that may not correspond to the real
one. We support our theoretical findings with the question of trend detection
in financial markets and show that the learned model performs better than
traditional practitioner methods and is less prone to overfitting.
