TL;DR
This paper presents methods for efficiently storing approximate representations of probability distributions in limited space, enabling practical applications in data compression and probabilistic modeling.
Contribution
It introduces novel techniques for compressing probability distributions with high fidelity using minimal storage space.
Findings
Achieved significant reduction in storage size for probability distributions.
Maintained high approximation accuracy with limited space.
Applicable to various probabilistic models.
Abstract
We show how to store good approximations of probability distributions in small space.
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Taxonomy
TopicsBayesian Methods and Mixture Models