Info-Metrics for Modeling and Inference

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,’ http://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.