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Computational inference of dated evolutionary histories relies upon various hypotheses about RNA, DNA, and protein sequence mutation rates. Using mutation rates to infer these dated histories is referred to as molecular clock assumption.…

Populations and Evolution · Quantitative Biology 2021-01-11 Lena Collienne , Kieran Elmes , Mareike Fischer , David Bryant , Alex Gavryushkin

Bayesian inference is now a leading technique for reconstructing phylogenetic trees from aligned sequence data. In this short note, we formally show that the maximum posterior tree topology provides a statistically consistent estimate of a…

Populations and Evolution · Quantitative Biology 2013-07-12 Mike Steel

Merge trees are fundamental structures in topological data analysis. Interleaving distance is a widely accepted metric for comparing merge trees, with applications in visualization and scientific computing. While a greedy algorithm exists…

Computational Geometry · Computer Science 2025-09-22 Elena Farahbakhsh Touli , Talha Bin Masood

Earth's forests play an important role in the fight against climate change, and are in turn negatively affected by it. Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of…

Computer Vision and Pattern Recognition · Computer Science 2025-07-30 Venkatesh Ramesh , Arthur Ouaknine , David Rolnick

Phylogenetic trees are simple models of evolutionary processes. They describe conditionally independent divergent evolution of taxa from common ancestors. Phylogenetic trees commonly do not have enough flexibility to adequately model all…

Populations and Evolution · Quantitative Biology 2025-11-11 Jonathan D. Mitchell , Barbara R. Holland

Genomes and genes diversify during evolution; however, it is unclear to what extent genes still retain the relationship among species. Model species for molecular phylogenetic studies include yeasts and viruses whose genomes were sequenced…

Genomics · Quantitative Biology 2008-06-09 Yunfeng Shan , Xiu-Qing Li

Many fundamental statistical methods have become critical tools for scientific data analysis yet do not scale tractably to modern large datasets. This paper will describe very recent algorithms based on computational geometry which have…

Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) and…

Machine Learning · Statistics 2021-10-25 Rafael Blanquero , Emilio Carrizosa , Cristina Molero-Río , Dolores Romero Morales

We consider the problem of drawing multiple gene trees inside a single species tree in order to visualize multispecies coalescent trees. Specifically, the drawing of the species tree fills a rectangle in which each of its edges is…

Discrete Mathematics · Computer Science 2022-11-01 Jonathan Klawitter , Felix Klesen , Moritz Niederer , Alexander Wolff

Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if…

Machine Learning · Computer Science 2019-08-14 Oktay Gunluk , Jayant Kalagnanam , Minhan Li , Matt Menickelly , Katya Scheinberg

Construction of phylogenetic trees and networks for extant species from their characters represents one of the key problems in phylogenomics. While solution to this problem is not always uniquely defined and there exist multiple methods for…

Populations and Evolution · Quantitative Biology 2016-08-10 Nikita Alexeev , Max A. Alekseyev

The structure of an evolving network contains information about its past. Extracting this information efficiently, however, is, in general, a difficult challenge. We formulate a fast and efficient method to estimate the most likely history…

Physics and Society · Physics 2020-09-16 Gábor Timár , Rui A. da Costa , Sergey N. Dorogovtsev , José F. F. Mendes

In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utility of two…

Computation and Language · Computer Science 2024-06-07 Ziru Chen , Michael White , Raymond Mooney , Ali Payani , Yu Su , Huan Sun

Divergence time estimation requires the reconciliation of two major sources of data. These are fossil and/or biogeographic evidence that give estimates of the absolute age of nodes (ancestors) and molecular estimates that give us estimates…

Populations and Evolution · Quantitative Biology 2008-12-31 Peter J Waddell

To introduce selection into a model of coalescence, I explore the use of modified integer partitions that allow the identification of a preferred lineage. I show that a partition-partition transition matrix, along with Monte Carlo discrete…

Populations and Evolution · Quantitative Biology 2017-01-05 Irwin Kuntz

In molecular systematics, evolutionary trees are reconstructed from sequences at the tips under simple models of site substitution. A central question is how much sequence data is required to reconstruct a tree accurately? The answer…

Quantitative Methods · Quantitative Biology 2012-07-18 Iain Martyn , Mike Steel

Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production. There had been a debate regarding the part of the tree to be used for differentiation, whether it…

Computer Vision and Pattern Recognition · Computer Science 2022-10-18 Sahil Faizal

Comparative analyses of phylogenetic trees typically require identical taxon sets, however, in practice, trees often include distinct but overlapping taxa. Pruning non-shared leaves discards phylogenetic signal, whereas tree completion can…

Populations and Evolution · Quantitative Biology 2026-04-28 Aleksandr Koshkarov , Nadia Tahiri

We consider the task of learning Ising models when the signs of different random variables are flipped independently with possibly unequal, unknown probabilities. In this paper, we focus on the problem of robust estimation of…

Machine Learning · Statistics 2020-06-11 Ashish Katiyar , Vatsal Shah , Constantine Caramanis

This paper presents a brand new nonparametric density estimation strategy named the best-scored random forest density estimation whose effectiveness is supported by both solid theoretical analysis and significant experimental performance.…

Machine Learning · Statistics 2019-05-10 Hanyuan Hang , Hongwei Wen
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