Info-Metrics for Modeling and Inference

Abstract:

Our classical statistical arsenal for extracting truth from data often fails to produce correct predictions. Uncertainty, blurry evidence and multiple possible solutions may trip up even the best interrogator. Info-metrics – the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information – provides a consistent and efficient framework for constructing models and theories with minimal assumptions. It reveals the simplest solution, model or story, that is hidden in the observed information. Technically, info-metrics is at the intersection of information-theory and statistical inference. It combines the tools and principles of information theory, within a constrained optimization framework.

My talk will be based on my new book ‘Foundations of Info-Metrics: Modeling, Inference, and Imperfect Information,’ info-metrics.org/ in which I develop and examine the theoretical underpinning of info-metrics and provide extensive interdisciplinary applications. In this talk I will discuss the basic ideas via a number of graphical representations of the model and theory, and will then present a number of interdisciplinary real-world examples of using that framework for modeling and inference. These examples include finance, network aggregation, predicting election, and more.