Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

The Bayesian view of extreme events

The Bayesian perspective of designing for the consequences of hazard is discussed. Structural engineers should be educated in Bayesian theory and its underlying philosophy, and about the centrality to the prediction problem of the predictive distribution. The primary contribution that Bayesianism can make to the debate about extreme possibilities is its clarification of the language of and thinking about risk. Frequentist methodologies are the wrong approach to the decisions that engineers need to make, decisions that involve assessments of abstract future possibilities based on incomplete and abstract information.