Join us Monday Sept 29 at 15 CET for a webinar with Jesper Løve Hinrich
The vast majority of tensor decomposition methods are based on least squares estimation – or equivalent maximum likelihood under a Gaussian distribution. In this presentation, I will introduce and motivate probabilistic tensor decomposition – based on Bayesian inference – and show the differences and similarities between the non-probabilistic and the probabilistic. Importantly, the probabilistic tensor decomposition methods are more robust to noise and model misspecification, provides new strategies for determining the right number of components, and in-build tools for characterizing model uncertainty. Some of these tools are already in the freely available probabilistic tensor toolbox https://github.com/JesperLH/prob-tensor-toolbox .