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arXiv · 1411.0030

A* Sampling

Chris J. Maddison·Daniel Tarlow·Tom Minka
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Abstract

The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A* search. We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.

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Chris J. Maddison, Daniel Tarlow, Tom Minka. 2015-01-26. A* Sampling. https://arxiv.org/abs/1411.0030

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