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Paper Citation Record · LEDGER

Likelihood-free inference of phylogenetic tree posterior distributions

As of 22 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2510.12976.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.12976 v4

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:57:00.115120Z

measured 46 of 46 standing notices

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T01:50:58.030760Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-06-30T03:04:14.432826Z

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 3835f0cf-f9d6-4bfd-be22-89a66b54b391 · outbound

This paper cites Aminov and Roderick I.

Likelihood-free inference of phylogenetic tree posterior distributions Aminov and Roderick I

Reference 1

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Likelihood-free inference of phylogenetic tree posterior distributions Unresolved cited work

Reference 2

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This paper cites The frontier of simulation-based inference.

Likelihood-free inference of phylogenetic tree posterior distributions The frontier of simulation-based inference

Reference 3

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Likelihood-free inference of phylogenetic tree posterior distributions Unresolved cited work

Reference 4

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This paper cites Evaluation of methods to detect shifts in directional selection at the genome scale.

Likelihood-free inference of phylogenetic tree posterior distributions Evaluation of methods to detect shifts in directional selection at the genome scale

Reference 5

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This paper cites Evolutionary trees from dna sequences: a maximum likelihood approach.

Likelihood-free inference of phylogenetic tree posterior distributions Evolutionary trees from dna sequences: a maximum likelihood approach

Reference 6

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This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Likelihood-free inference of phylogenetic tree posterior distributions Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 7

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Observation 531176f5-a7a7-4487-ade4-350523ae843a · outbound

This paper cites Automatic posterior transformation for likelihood-free inference.

Likelihood-free inference of phylogenetic tree posterior distributions Automatic posterior transformation for likelihood-free inference

Reference 8

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This paper cites Grosshauser M, Zaharias P.

Likelihood-free inference of phylogenetic tree posterior distributions Grosshauser M, Zaharias P

Reference 9

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This paper cites Nextstrain: real-time tracking of pathogen evolution.

Likelihood-free inference of phylogenetic tree posterior distributions Nextstrain: real-time tracking of pathogen evolution

Reference 10

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This paper cites Axial Attention in Multidimensional Transformers.

Likelihood-free inference of phylogenetic tree posterior distributions Axial Attention in Multidimensional Transformers

Reference 11

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This paper cites Huelsenbeck and Fredrik Ronquist.

Likelihood-free inference of phylogenetic tree posterior distributions Huelsenbeck and Fredrik Ronquist

Reference 12

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This paper cites Landis, Tracy A.

Likelihood-free inference of phylogenetic tree posterior distributions Landis, Tracy A

Reference 13

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This paper cites Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach.

Likelihood-free inference of phylogenetic tree posterior distributions Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach

Reference 14

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Likelihood-free inference of phylogenetic tree posterior distributions Unresolved cited work

Reference 15

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Likelihood-free inference of phylogenetic tree posterior distributions Unresolved cited work

Reference 16

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This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention, 2020.

Likelihood-free inference of phylogenetic tree posterior distributions Transformers are rnns: Fast autoregressive transformers with linear attention, 2020

Reference 17

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Likelihood-free inference of phylogenetic tree posterior distributions Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 18

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This paper cites A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process.

Likelihood-free inference of phylogenetic tree posterior distributions A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process

Reference 19

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Likelihood-free inference of phylogenetic tree posterior distributions Inferring long-term effective population size with mutation–selection models

Reference 20

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Likelihood-free inference of phylogenetic tree posterior distributions An Improved General Amino Acid Replacement Matrix

Reference 21

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This paper cites Fastme 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program.

Likelihood-free inference of phylogenetic tree posterior distributions Fastme 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program

Reference 22

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Likelihood-free inference of phylogenetic tree posterior distributions Benchmarking simulation-based inference

Reference 23

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Likelihood-free inference of phylogenetic tree posterior distributions Iq-tree 2: New models and efficient methods for phylogenetic inference in the genomic era

Reference 24

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This paper cites Muñoz-Gómez, Edward Susko, Kelsey Williamson, Laura Eme, Claudio H.

Likelihood-free inference of phylogenetic tree posterior distributions Muñoz-Gómez, Edward Susko, Kelsey Williamson, Laura Eme, Claudio H

Reference 25

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Likelihood-free inference of phylogenetic tree posterior distributions Phyloformer: Fast, accurate, and versatile phylogenetic reconstruction with deep neural networks

Reference 26

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Likelihood-free inference of phylogenetic tree posterior distributions Price, Paramvir S

Reference 27

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Likelihood-free inference of phylogenetic tree posterior distributions Unresolved cited work

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Likelihood-free inference of phylogenetic tree posterior distributions Radev, Ulf K

Reference 29

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Likelihood-free inference of phylogenetic tree posterior distributions Msa transformer

Reference 30

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Likelihood-free inference of phylogenetic tree posterior distributions Robinson and L.R

Reference 31

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Likelihood-free inference of phylogenetic tree posterior distributions The neighbor-joining method: a new method for reconstructing phylogenetic trees

Reference 32

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Likelihood-free inference of phylogenetic tree posterior distributions Accurate Inference of Tree Topologies from Multiple Sequence Alignments Using Deep Learning

Reference 33

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Likelihood-free inference of phylogenetic tree posterior distributions Novel symmetry-preserving neural network model for phylogenetic inference

Reference 34

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This paper cites Consideration of RNA Secondary Structure Significantly Improves Likelihood - Based Estimates of Phylogeny : Examples from the Bilateria.

Likelihood-free inference of phylogenetic tree posterior distributions Consideration of RNA Secondary Structure Significantly Improves Likelihood - Based Estimates of Phylogeny : Examples from the Bilateria

Reference 35

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Likelihood-free inference of phylogenetic tree posterior distributions Simulations of sequence evolution: how (un) realistic they are and why

Reference 36

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Likelihood-free inference of phylogenetic tree posterior distributions Simplefold: Folding proteins is simpler than you think, 2025

Reference 37

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Likelihood-free inference of phylogenetic tree posterior distributions Supertree Construction: Opportunities and Challenges

Reference 38

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This paper cites Minifold: Simple, fast, and accurate protein structure prediction.

Likelihood-free inference of phylogenetic tree posterior distributions Minifold: Simple, fast, and accurate protein structure prediction

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This paper cites ART ree: A deep autoregressive model for phylogenetic inference.

Likelihood-free inference of phylogenetic tree posterior distributions ART ree: A deep autoregressive model for phylogenetic inference

Reference 40

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This paper cites Matsen IV.

Likelihood-free inference of phylogenetic tree posterior distributions Matsen IV

Reference 41

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This paper cites Phylo GFN : Phylogenetic inference with generative flow networks.

Likelihood-free inference of phylogenetic tree posterior distributions Phylo GFN : Phylogenetic inference with generative flow networks

Reference 42

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This paper cites Deep residual neural networks resolve quartet molecular phylogenies.

Likelihood-free inference of phylogenetic tree posterior distributions Deep residual neural networks resolve quartet molecular phylogenies

Reference 43

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This paper cites Tamuri, Matteo Battini, Fabrícia F.

Likelihood-free inference of phylogenetic tree posterior distributions Tamuri, Matteo Battini, Fabrícia F

Reference 44

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Pith citing papers

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Phylogenetic Tree Inference with Tropical Axial Attention cites this paper.

Phylogenetic Tree Inference with Tropical Axial Attention Likelihood-free inference of phylogenetic tree posterior distributions

Reference 4

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Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics cites this paper.

Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics Likelihood-free inference of phylogenetic tree posterior distributions

Reference 156

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