TL;DR
This paper addresses how to modify quasi-probability kernels to enhance their properties while preserving the set of measures they define, aiming to improve their mathematical characteristics without altering their fundamental probabilistic interpretations.
Contribution
It introduces methods for refining quasi-probability kernels to optimize their properties without changing the associated measure set.
Findings
Proposed a systematic approach for kernel refinement.
Maintained measure set invariance during modification.
Enhanced kernel properties for better mathematical handling.
Abstract
We consider the problem of modifying a quasi-probability kernel in order to improve its properties without changing the set of measures whose conditional probabilities it specifies.
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Taxonomy
TopicsAdvanced Computational Techniques in Science and Engineering · Advanced Data Processing Techniques · Mathematical Control Systems and Analysis