Boltzmann Sampling for Powersets without an Oracle
Jean Peyen
TL;DR
This paper introduces an efficient method for sampling powersets over structures with bounded counting sequences without needing an oracle, supported by an algorithm implementation and empirical testing.
Contribution
It presents a novel algorithm for powerset sampling that operates without an oracle, expanding the applicability of Boltzmann sampling techniques.
Findings
Runtimes comparable to existing Boltzmann samplers
Algorithm successfully implemented and tested
Sampling achieved without evaluating the generating function
Abstract
We show that powersets over structures with a bounded counting sequence can be sampled efficiently without evaluating the generating function. An algorithm is provided, implemented, and tested. Runtimes are comparable to existing Boltzmann samplers reported in the literature.
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Statistical Mechanics and Entropy · Generative Adversarial Networks and Image Synthesis