A First Step Towards Even More Sparse Encodings of Probability Distributions
This addresses the issue of exponential storage requirements for probability distributions in real-world scenarios, though it appears incremental as it builds on existing encoding methods.
The paper tackles the problem of encoding probability distributions more efficiently by proposing a method to extract first-order formulas from distributions, which reduces the number of values needed and increases sparsity while preserving core information.
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.