The Automated Statistician

A project aimed at:

• Automating feature selection, transformation and understanding.
• Automating data collection and experiment design.
• Automating model discovery and explanation (Important aim).
• Automating allocation of computational resources.
• Automating inference.

Project was motivated due to a lack of data scientists, machine learning experts etc, thus creating a need for a system that automates model discovery from data.

Main ingredients:

• An open-ended language of models (#DOUBT I guess graphical models?)
• A search procedure over the language of models.
• A principled approach of evaluating models.
• A procedure to automatically explain the models, i.e. make the assumptions more explicit and communicate them to non-experts.

For regression in time-series data, the search procedure involves creating a decision tree whose nodes correspond Gaussian process kernel combinations and the quantity maximized is the marginal likelihood.