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Tree trace reconstruction using subtraces

Part of: Combinatorial probability Fuzzy sets and logic

Published online by Cambridge University Press:  15 December 2022

Tatiana Brailovskaya Open the ORCID record for Tatiana Brailovskaya [Opens in a new window]  and
Miklós Z. Rácz
Tatiana Brailovskaya*
Affiliation:
Princeton University
Miklós Z. Rácz*
Affiliation:
Princeton University
*
*Postal address: Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA. Email address: tatianab@princeton.edu
**Postal address: Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ, USA. Email address: mracz@princeton.edu
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Abstract

Tree trace reconstruction aims to learn the binary node labels of a tree, given independent samples of the tree passed through an appropriately defined deletion channel. In recent work, Davies, Rácz, and Rashtchian [10] used combinatorial methods to show that $\exp({\mathrm{O}} (k \log_{k} n))$ samples suffice to reconstruct a complete k-ary tree with n nodes with high probability. We provide an alternative proof of this result, which allows us to generalize it to a broader class of tree topologies and deletion models. In our proofs we introduce the notion of a subtrace, which enables us to connect with and generalize recent mean-based complex analytic algorithms for string trace reconstruction.

Keywords

Trace reconstructionstatistical error correctiontree graphs

MSC classification

Primary: 60C05: Combinatorial probability
Secondary: 94D99: None of the above, but in this section

Information

Type
Original Article
Information
Journal of Applied Probability , Volume 60 , Issue 2 , June 2023 , pp. 629 - 641
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Applied Probability Trust

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