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Documents authored by Lladser, Manuel E.

Document
DOI: 10.4230/LIPIcs.AofA.2024.4
Sparsification of Phylogenetic Covariance Matrices of k-Regular Trees

Authors: Sean Svihla and Manuel E. Lladser

Published in: LIPIcs, Volume 302, 35th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2024)

Abstract
Consider a tree T = (V,E) with root ∘ and an edge length function 𝓁:E → ℝ_+. The phylogenetic covariance matrix of T is the matrix C with rows and columns indexed by L, the leaf set of T, with entries C(i,j): = ∑_{e ∈ [i∧ j,o]}𝓁(e), for each i,j ∈ L. Recent work [Gorman & Lladser 2023] has shown that the phylogenetic covariance matrix of a large but random binary tree T is significantly sparsified, with overwhelmingly high probability, under a change-of-basis to the so-called Haar-like wavelets of T. Notably, this finding enables manipulating the spectrum of covariance matrices of large binary trees without the necessity to store them in computer memory but instead performing two post-order traversals of the tree [Gorman & Lladser 2023]. Building on the methods of the aforesaid paper, this manuscript further advances their sparsification result to encompass the broader class of k-regular trees, for any given k ≥ 2. This extension is achieved by refining existing asymptotic formulas for the mean and variance of the internal path length of random k-regular trees, utilizing hypergeometric function properties and identities.
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Sean Svihla and Manuel E. Lladser. Sparsification of Phylogenetic Covariance Matrices of k-Regular Trees. In 35th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 302, pp. 4:1-4:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{svihla_et_al:LIPIcs.AofA.2024.4,
  author =	{Svihla, Sean and Lladser, Manuel E.},
  title =	{{Sparsification of Phylogenetic Covariance Matrices of k-Regular Trees}},
  booktitle =	{35th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2024)},
  pages =	{4:1--4:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-329-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{302},
  editor =	{Mailler, C\'{e}cile and Wild, Sebastian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AofA.2024.4},
  URN =		{urn:nbn:de:0030-drops-204399},
  doi =		{10.4230/LIPIcs.AofA.2024.4},
  annote =	{Keywords: cophenetic matrix, Haar-like wavelets, hierarchical data, hypergeometric functions, metagenomics, phylogenetic covariance matrix, sparsification, ultrametric matrix}
}
Document
DOI: 10.4230/LIPIcs.AofA.2020.23
Hidden Independence in Unstructured Probabilistic Models

Authors: Antony Pearson and Manuel E. Lladser

Published in: LIPIcs, Volume 159, 31st International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2020)

Abstract
We describe a novel way to represent the probability distribution of a random binary string as a mixture having a maximally weighted component associated with independent (though not necessarily identically distributed) Bernoulli characters. We refer to this as the latent independent weight of the probabilistic source producing the string, and derive a combinatorial algorithm to compute it. The decomposition we propose may serve as an alternative to the Boolean paradigm of hypothesis testing, or to assess the fraction of uncorrupted samples originating from a source with independent marginal distributions. In this sense, the latent independent weight quantifies the maximal amount of independence contained within a probabilistic source, which, properly speaking, may not have independent marginal distributions.
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Antony Pearson and Manuel E. Lladser. Hidden Independence in Unstructured Probabilistic Models. In 31st International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 159, pp. 23:1-23:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{pearson_et_al:LIPIcs.AofA.2020.23,
  author =	{Pearson, Antony and Lladser, Manuel E.},
  title =	{{Hidden Independence in Unstructured Probabilistic Models}},
  booktitle =	{31st International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2020)},
  pages =	{23:1--23:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-147-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{159},
  editor =	{Drmota, Michael and Heuberger, Clemens},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AofA.2020.23},
  URN =		{urn:nbn:de:0030-drops-120538},
  doi =		{10.4230/LIPIcs.AofA.2020.23},
  annote =	{Keywords: Bayesian networks, contamination, latent weights, mixture models, independence, symbolic data}
}
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