Continuously-adaptive discretization for message-passing algorithms

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Michael Isard, John MacCormick, Kannan Achan


Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passing algorithm employing adaptive discretization. Most previous message-passing algorithms approximated arbitrary continuous probability distributions using either: a family of continuous distributions such as the exponential family; a particle-set of discrete samples; or a fixed, uniform discretization. In contrast, CAD-MP uses a discretization that is (i) non-uniform, and (ii) adaptive. The non-uniformity allows CAD-MP to localize interesting features (such as sharp peaks) in the marginal belief distributions with time complexity that scales logarithmically with precision, as opposed to uniform discretization which scales at best linearly. We give a principled method for altering the non-uniform discretization according to information-based measures. CAD-MP is shown in experiments on simulated data to estimate marginal beliefs much more precisely than competing approaches for the same computational expense.