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Poster
Black-box density function estimation using recursive partitioning
Erik Bodin · Zhenwen Dai · Neill Campbell · Carl Henrik Ek

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @
in Poster Session 6 »

We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop. Our method defines a recursive partitioning of the sample space. It neither relies on gradients nor requires any problem-specific tuning, and is asymptotically exact for any density function with a bounded domain. The output is an approximation to the whole density function including the normalisation constant, via partitions organised in efficient data structures. Such approximations may be used for evidence estimation or fast posterior sampling, but also as building blocks to treat a larger class of estimation problems. The algorithm shows competitive performance to recent state-of-the-art methods on synthetic and real-world problems including parameter inference for gravitational-wave physics.

Keywords: Approximate Inference

Author Information

Erik Bodin (The Alan Turing Institute)
Zhenwen Dai (Spotify)
Neill Campbell (University of Bath)
Carl Henrik Ek (University of Cambridge)

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