Approximate independence of permutation mixtures
<|reference_start|>Approximate independence of permutation mixtures: We prove bounds on statistical distances between high-dimensional exchangeable mixture distributions (which we call permutation mixtures) and their i.i.d. counterparts. Our results are based on a novel method for controlling $\chi^2$ divergences between exchangeable mixtures, which is tighter than the existing methods of moments or cumulants. At a technical level, a key innovation in our proofs is a new Maclaurin-type inequality for elementary symmetric polynomials of variables that sum to zero and an upper bound on permanents of doubly-stochastic positive semidefinite matrices. Our results imply a de Finetti-style theorem (in the language of Diaconis and Freedman, 1987) and general asymptotic results for compound decision problems, generalizing and strengthening a result of Hannan and Robbins (1955).<|reference_end|>
@article{han2024approximate,
    title={Approximate independence of permutation mixtures},
    author={Yanjun Han, Jonathan Niles-Weed},
    journal={arXiv preprint arXiv:2408.09341},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09341},
    primaryClass={math.ST cs.IT math.IT math.PR stat.TH}
}