E. Allman

发表

Identifiability of parameters in latent structure models with many observed variables

C. Matias, E. Allman, J. Rhodes, 2008, 0809.5032.

Phylogenetic invariants for the general Markov model of sequence mutation.

E. Allman, J. Rhodes, 2003, Mathematical biosciences.

Parameter identifiability in a class of random graph mixture models

C. Matias, E. Allman, J. Rhodes, 2010, 1006.0826.

Identifiability of a Markovian model of molecular evolution with gamma-distributed rates

Elizabeth S. Allman, John A. Rhodes, Cecile Ane, 2007, Advances in Applied Probability.

Split Scores: A Tool to Quantify Phylogenetic Signal in Genome‐Scale Data

John A Rhodes, Elizabeth S Allman, Laura S Kubatko, 2016, Systematic biology.

Species Tree Inference from Gene Splits by Unrooted STAR Methods.

John A Rhodes, Elizabeth S Allman, E. Allman, 2018, IEEE/ACM transactions on computational biology and bioinformatics.

Determining species tree topologies from clade probabilities under the coalescent.

John A Rhodes, Elizabeth S Allman, E. Allman, 2011, Journal of theoretical biology.

Maximum likelihood estimation of the Latent Class Model through model boundary decomposition

Piotr Zwiernik, Elizabeth S. Allman, John A. Rhodes, 2017, Journal of Algebraic Statistics.

Split Probabilities and Species Tree Inference Under the Multispecies Coalescent Model

John A Rhodes, Elizabeth S Allman, E. Allman, 2017, Bulletin of mathematical biology.

Quartets and Parameter Recovery for the General Markov Model of Sequence Mutation

E. Allman, J. Rhodes, 2004 .

MOLECULAR PHYLOGENETICS FROM AN ALGEBRAIC VIEWPOINT

E. Allman, J. Rhodes, 2007 .

Bibliography [1] Linda Allen, Stochastic Processes with Applications to Biology, Pearson, New

E. Allman, J. Rhodes, T. Kurtz, 2011 .

Maximum Likelihood Estimates for Binary Random Variables on Trees via Phylogenetic Ideals

E. Allman, J. Rhodes, 2009 .

Gene tree discord, simplex plots, and statistical tests under the coalescent

Jonathan D. Mitchell, E. Allman, J. Rhodes, 2020, bioRxiv.

The tree of blobs of a species network: identifiability under the coalescent

Jonathan D. Mitchell, E. Allman, J. Rhodes, 2022, Journal of Mathematical Biology.

Lecture Notes: The Mathematics of Phylogenetics

E. Allman, J. Rhodes, 2005 .

Identifiability of the Gtr+γ Model of Molecular Evolution

E. Allman, J. Rhodes, C. Ané, 2008 .

Tensors of Nonnegative Rank Two

E. Allman, J. Rhodes, B. Sturmfels, 2013, 1305.0539.

Parameter Identifiability of a Multitype Pure-Birth Model of Speciation

E. Allman, J. Rhodes, Dakota Dragomir, 2022, J. Comput. Biol..

A generalized AIC for models with singularities and boundaries

Jonathan D. Mitchell, E. Allman, J. Rhodes, 2022, 2211.04136.

Testing Multispecies Coalescent Simulators using Summary Statistics.

E. Allman, J. Rhodes, H. Baños, 2019, IEEE/ACM transactions on computational biology and bioinformatics.

MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

Jonathan D. Mitchell, E. Allman, J. Rhodes, 2020, bioRxiv.

NANUQ: a method for inferring species networks from gene trees under the coalescent model

John Rhodes, Elizabeth Allman, Hector Banos, 2019, Algorithms for Molecular Biology.

Estimating trees from filtered data: identifiability of models for morphological phylogenetics.

John A Rhodes, Elizabeth S Allman, E. Allman, 2009, Journal of theoretical biology.

Mathematical Models in Biology - Introduction

Elizabeth S. Allman, John A. Rhodes, E. Allman, 2004 .

Trees, Fast and Accurate

John A Rhodes, Elizabeth S Allman, E. Allman, 2010, Science.

Species Tree Inference from Gene Splits by Unrooted STAR Methods

James H. Degnan, Elizabeth S. Allman, John A. Rhodes, 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Species Tree Inference by the STAR Method and Its Generalizations

James H. Degnan, Elizabeth S. Allman, John A. Rhodes, 2013, J. Comput. Biol..

The Identifiability of Covarion Models in Phylogenetics

Elizabeth S. Allman, John A. Rhodes, E. Allman, 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

The Identifiability of Tree Topology for Phylogenetic Models, Including Covarion and Mixture Models

Elizabeth S. Allman, John A. Rhodes, E. Allman, 2005, J. Comput. Biol..

PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow

E. Allman, J. Fogg, C. Ané, 2023, bioRxiv.

Identifiability of species network topologies from genomic sequences using the logDet distance

Elizabeth S. Allman, John A. Rhodes, Hector Banos, 2021, Journal of Mathematical Biology.

Hypothesis testing near singularities and boundaries.

Jonathan D. Mitchell, E. Allman, J. Rhodes, 2018, Electronic journal of statistics.

Identifying the rooted species tree from the distribution of unrooted gene trees under the coalescent

John A Rhodes, Elizabeth S Allman, E. Allman, 2009, Journal of mathematical biology.

Species Tree Inference from Genomic Sequences Using the Log-Det Distance

Colby Long, Elizabeth S. Allman, John A. Rhodes, 2018, SIAM J. Appl. Algebra Geom..

Tensor Rank, Invariants, Inequalities, and Applications

Peter D. Jarvis, Jeremy G. Sumner, Elizabeth S. Allman, 2012, SIAM J. Matrix Anal. Appl..

Parameter Identifiability for a Profile Mixture Model of Protein Evolution

Elizabeth S. Allman, John A. Rhodes, Samaneh Yourdkhani, 2020, J. Comput. Biol..

Parameter Identifiability of Discrete Bayesian Networks with Hidden Variables

Marco Valtorta, Elena Stanghellini, Elizabeth S. Allman, 2014, 1406.0541.

A Semialgebraic Description of the General Markov Model on Phylogenetic Trees

Amelia Taylor, Elizabeth S. Allman, John A. Rhodes, 2012, SIAM J. Discret. Math..

Identifiability of latent class models with many observed variables

Elizabeth S. Allman, John A. Rhodes, Catherine Matias, 2008 .

Gene tree discord, simplex plots, and statistical tests under the coalescent.

Jonathan D. Mitchell, E. Allman, J. Rhodes, 2021, Systematic biology.

Split Probabilities and Species Tree Inference Under the Multispecies Coalescent Model

E. Allman, J. Rhodes, J. Degnan, 2017, Bulletin of Mathematical Biology.

Species tree inference by the STAR method, and generalizations

E. Allman, J. Rhodes, J. Degnan, 2012, 1204.4413.

Mathematical models in biology : an introduction

Elizabeth S. Allman, John A. Rhodes, E. Allman, 2003 .

Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction

Seth Sullivant, Elizabeth S. Allman, John A. Rhodes, 2015, J. Comput. Biol..

Identifiability of Two-Tree Mixtures for Group-Based Models

Seth Sullivant, Elizabeth S. Allman, John A. Rhodes, 2009, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Phylogenetic ideals and varieties for the general Markov model

Elizabeth S. Allman, John A. Rhodes, E. Allman, 2004, Adv. Appl. Math..

ANOMALOUS NETWORKS UNDER THE MULTISPECIES COALESCENT: THEORY AND PREVALENCE

E. Allman, J. Rhodes, H. Baños, 2023, bioRxiv.

Phylogenetic invariants for stationary base composition

Elizabeth S. Allman, John A. Rhodes, E. Allman, 2004, J. Symb. Comput..

Hypothesis testing near singularities and boundaries.

Jonathan D. Mitchell, E. Allman, J. Rhodes, 2018, Electronic journal of statistics.

When do phylogenetic mixture models mimic other phylogenetic models?

Seth Sullivant, John A Rhodes, Elizabeth S Allman, 2012, Systematic biology.

Identifiability of Two-Tree Mixtures for Group-Based Models

E. Allman, J. Rhodes, S. Sullivant, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Identifiability of latent class models with many observed variables

C. Matias, E. Allman, J. Rhodes, 2008 .

Division Algebras with PSL(2, q)-Galois Maximal Subfields

E. Allman, M. Schacher, 2001 .

Polynomials without roots in division algebras

E. Allman, 1996 .

Abelian Subgroups of Finitely Generated Kleinian Groups are Separable

E. Allman, E. Hamilton, 1999 .

Identifying evolutionary trees and substitution parameters for the general Markov model with invariable sites.

John A Rhodes, Elizabeth S Allman, E. Allman, 2007, Mathematical biosciences.