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arXiv · 2603.29691

A First Step Towards Even More Sparse Encodings of Probability Distributions

Florian Andreas Marwitz·Tanya Braun·Ralf Möller
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Abstract

Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information.

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Florian Andreas Marwitz, Tanya Braun, Ralf Möller. 2026-03-31. A First Step Towards Even More Sparse Encodings of Probability Distributions. https://doi.org/10.1007/978-3-030-97454-1_13

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