Analysis of the most recent modelling techniques for big data with particular attention to Bayesian ones : 2018 edition

In this report we describe various methods suited for the analysis of linear models with a very large number of explanatory variables, with a special emphasis on Bayesian approaches. We next consider some non-parametric and/or non-linear methods suited for applications with big data, such as random trees, random forests, cluster analysis, deep learning and neural networks. Finally, we survey techniques for summarizing the information in large (possibly sparse) datasets, forecast combination approaches, and techniques for the analysis of large mixed frequency datasets.