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Graphical models for statistical inference and data assimilation

A. Ihler,Sergey Kirshner,M. Ghil,A. Robertson,Padhraic Smyth

Published 2007 in Physica A: Statistical Mechanics and its Applications

ABSTRACT

In data assimilation for a system which evolves in time, one combines past and current observations with a model of the dynamics of the system, in order to improve the simulation of the system as well as any future predictions about it. From a statistical point of view, this process can be regarded as estimating many random variables, which are related both spatially and temporally: given observations of some of these variables, typically corresponding to times past, we require estimates of several others, typically corresponding to future times. Graphical models have emerged as an efiective formalism for assisting in these types of inference tasks, particularly for large numbers of random variables. Graphical models provide a means of representing dependency structure among the variables, and can provide both intuition and e‐ciency in estimation and other inference computations. We provide an overview and introduction to graphical models, and describe how they can be used to represent statistical dependency and how the resulting structure can be used to organize computation. The relation between statistical inference using graphical models and optimal sequential estimation algorithms such as Kalman flltering is discussed. We then give several additional examples of how graphical models can be applied to climate dynamics, speciflcally estimation using multi-resolution models of large{scale data sets such as satellite imagery, and learning hidden Markov models to capture rainfall patterns in space and time.

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