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Population genetics theory has laid the foundations for genomics analyses including the recent burst in genome scans for selection and statistical inference of past demographic events in many prokaryote, animal and plant species.…

Populations and Evolution · Quantitative Biology 2014-01-22 Aurelien Tellier , Christophe Lemaire

We establish convergence to the Kingman coalescent for the genealogy of a geographically - or otherwise - structured version of the Wright-Fisher population model with fast migration. The new feature is that migration probabilities may…

Probability · Mathematics 2010-06-25 Serik Sagitov , Peter Jagers , Vladimir Vatutin

The observed sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular sequence variation to infer evolutionary…

Methodology · Statistics 2021-06-22 Lorenzo Cappello , Amandine Veber , Julia A. Palacios

Evolutionary models for populations of constant size are frequently studied using the Moran model, the Wright-Fisher model, or their diffusion limits. When evolution is neutral, a random genealogy given through Kingman's coalescent is used…

Populations and Evolution · Quantitative Biology 2012-07-31 Peter Pfaffelhuber , Benedikt Vogt

The correct characterization of uncertainty when predicting human motion is equally important as the accuracy of this prediction. We present a new method to correctly predict the uncertainty associated with the predicted distribution of…

Computer Vision and Pattern Recognition · Computer Science 2021-12-14 Aleksey Postnikov , Aleksander Gamayunov , Gonzalo Ferrer

We propose in this article a brief description of the work, over almost a decade, resulting from a collaboration between mathematicians and biologists from four different research laboratories, identifiable as the co-authors of the articles…

Populations and Evolution · Quantitative Biology 2023-05-30 Olivier Mazet , Camille Noûs

We introduce a broad class of spatial models to describe how spatially heterogeneous populations live, die, and reproduce. Individuals are represented by points of a point measure, whose birth and death rates can depend both on spatial…

Probability · Mathematics 2024-01-02 Alison M. Etheridge , Thomas G. Kurtz , Ian Letter , Peter L. Ralph , Terence Tsui Ho Lung

We propose a Bayesian nonparametric (BNP) approach to causal inference using observational data consisting of outcome, treatment, and a set of confounders. The conditional distribution of the outcome given treatment and confounders is…

Methodology · Statistics 2025-12-01 Yongseok Hur , Joonhyuk Jung , Juhee Lee

We consider a dynamic metapopulation involving one large population of size N surrounded by colonies of size \varepsilon_NN, usually called peripheral isolates in ecology, where N\to\infty and \varepsilon_N\to 0 in such a way that…

Probability · Mathematics 2013-03-15 Amaury Lambert , Chunhua Ma

Optimizing wheat variety selection for high performance in different environmental conditions is critical for reliable food production and stable incomes for growers. We employ a statistical machine learning framework utilizing Gaussian…

Many populations, e.g. of cells, bacteria, viruses, or replicating DNA molecules, start small, from a few individuals, and grow large into a noticeable fraction of the environmental carrying capacity $K$. Typically, the elements of the…

Probability · Mathematics 2018-06-12 P. Chigansky , P. Jagers , F. C. Klebaner

The coalescent revolutionised theoretical population genetics, simplifying, or making possible for the first time, many analyses, proofs, and derivations, and offering crucial insights about the way in which the structure of data in samples…

Methodology · Statistics 2010-06-09 Peter Donnelly , Stephen Leslie

This paper investigates a nonlinear logistic model for age-structured population dynamics. The model incorporates interdependent fertility and mortality functions within a logistic framework, offering insights into stationary solutions and…

Analysis of PDEs · Mathematics 2025-11-24 Dragos-Patru Covei

Many population genetic models have been developed for the purpose of inferring population size and growth rates from random samples of genetic data. We examine two popular approaches to this problem, the coalescent and the…

Populations and Evolution · Quantitative Biology 2014-08-29 Erik M. Volz , Simon DW Frost

1) Micro-evolutionary predictions are complicated by ecological feedbacks like density dependence, while ecological predictions can be complicated by evolutionary change. A widely used approach in micro-evolution, quantitative genetics,…

Populations and Evolution · Quantitative Biology 2015-09-07 Tim Coulson , Floriane Plard , Susanne Schindler , Arpat Ozgul , Jean-Michel Gaillard

Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing…

Machine Learning · Computer Science 2025-10-28 Zheng Li , Xichen Guo , Feng Xie , Yan Zeng , Hao Zhang , Zhi Geng

Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting…

To learn about the past from a sample of genomic sequences, one needs to understand how evolutionary processes shape genetic diversity. Most population genetic inference is based on frameworks assuming adaptive evolution is rare. But if…

Populations and Evolution · Quantitative Biology 2014-03-25 Richard A. Neher

The goal of phylodynamics, an area on the intersection of phylogenetics and population genetics, is to reconstruct population size dynamics from genetic data. Recently, a series of nonparametric Bayesian methods have been proposed for such…

Applications · Statistics 2012-10-19 Julia A. Palacios , Vladimir N. Minin

Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approaches. In this paper we…