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 (O(k logk 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.
Original language | English (US) |
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Pages (from-to) | 629-641 |
Number of pages | 13 |
Journal | Journal of Applied Probability |
Volume | 60 |
Issue number | 2 |
DOIs | |
State | Published - 2023 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- General Mathematics
- Statistics, Probability and Uncertainty
Keywords
- Trace reconstruction
- statistical error correction
- tree graphs