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Statistical hypothesis tests typically use prespecified sample sizes, yet data often arrive sequentially. Interim analyses invalidate classical error guarantees, while existing sequential methods require rigid testing preschedules or incur…

Methodology · Statistics 2026-02-17 Chris Holmes , Stephen Walker

We study the stability of non-conservative deterministic cross diffusion models and prove that they are approximated by stochastic population models when the populations become locally large. In this model, the individuals of two species…

Analysis of PDEs · Mathematics 2025-10-09 Vincent Bansaye , Alexandre Bertolino , Ayman Moussa

The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions…

Neurons and Cognition · Quantitative Biology 2017-06-02 Ulisse Ferrari , Tomoyuki Obuchi , Thierry Mora

Possible parameter values in a random sampling model are shown by definition to have uniform base-rate prior probabilities. This allows a frequentist posterior probability distribution to be calculated for such possible parameter values…

Other Statistics · Statistics 2020-02-14 Huw Llewelyn

Study samples often differ from the target populations of inference and policy decisions in non-random ways. Researchers typically believe that such departures from random sampling -- due to changes in the population over time and space, or…

Methodology · Statistics 2023-07-20 Tamara Broderick , Ryan Giordano , Rachael Meager

Symmetry arguments are frequently used -- often implicitly -- in mathematical modeling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce…

Populations and Evolution · Quantitative Biology 2023-07-14 Benjamin Allen

A new method based on the rejection sampling for finding statistical tests is proposed. This method is conceptually intuitive, easy to implement, and applicable for arbitrary dimension. To illustrate its potential applicability, three…

Methodology · Statistics 2026-03-11 Markku Kuismin

The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting a good subset. We formulate…

Optimization and Control · Mathematics 2023-05-09 Gongbo Zhang , Bin Chen , Qing-shan Jia , Yijie Peng

The $T$-test is probably the most popular statistical test; it is routinely recommended by the textbooks. The applicability of the test relies upon the validity of normal or Student's approximation to the distribution of Student's statistic…

Statistics Theory · Mathematics 2021-01-01 S. Y. Novak

In this paper, a new modification of ranked set sampling (RSS) is suggested, namely; unified ranked set sampling (URSS) for estimating the population mean and variance. The performance of the empirical mean and variance estimators based on…

Methodology · Statistics 2015-10-07 Ehsan Zamanzade , Amer Ibrahim Al-Omari

In this paper we consider two populations whose generations are not overlapping and whose size is large. The number of males and females in both populations is constant. Any generation is replaced by a new one and any individual has two…

Populations and Evolution · Quantitative Biology 2009-11-13 Maurizio Serva

Environmental changes greatly influence the evolution of populations. Here, we study the dynamics of a population of two strains, one growing slightly faster than the other, competing for resources in a time-varying binary environment…

Populations and Evolution · Quantitative Biology 2020-07-28 Ami Taitelbaum , Robert West , Michael Assaf , Mauro Mobilia

Comparisons of different treatments or production processes are the goals of a significant fraction of applied research. Unsurprisingly, two-sample problems play a main role in Statistics through natural questions such as `Is the the new…

Methodology · Statistics 2017-09-05 P. C. Álvarez-Esteban , E. del Barrio , J. A. Cuesta-Albertos , C. Matrán

We model the joint distribution of choice probabilities and decision times in binary choice tasks as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant…

Neurons and Cognition · Quantitative Biology 2015-05-14 Drew Fudenberg , Philipp Strack , Tomasz Strzalecki

In "Recognizing the Maximum of a Sequence", Gilbert and Mosteller analyze a full information game where n measurements from an uniform distribution are drawn and a player (knowing n) must decide at each draw whether or not to choose that…

Probability · Mathematics 2018-05-30 Marcos Costa Santos Carreira

We review models of biological evolution in which the population frequency changes deterministically with time. If the population is self-replicating, although the equations for simple prototypes can be linearised, nonlinear equations arise…

Populations and Evolution · Quantitative Biology 2015-05-27 Kavita Jain , Sarada Seetharaman

We study a model of a population making a binary decision based on information spreading within the population, which is fully connected or covering a square grid. We assume that a fraction of the population wants to make the choice of the…

Physics and Society · Physics 2016-05-24 Petter Holme , Hang-Hyun Jo

Non-African populations have experienced major bottlenecks in the time since their split from West Africans, which has led to the hypothesis that natural selection to remove weakly deleterious mutations may have been less effective in…

Populations and Evolution · Quantitative Biology 2014-02-21 Ron Do , Daniel Balick , Heng Li , Ivan Adzhubei , Shamil Sunyaev , David Reich

We consider a model of a population of fixed size $N$ undergoing selection. Each individual acquires beneficial mutations at rate $\mu_N$, and each beneficial mutation increases the individual's fitness by $s_N$. Each individual dies at…

Probability · Mathematics 2015-07-03 Jason Schweinsberg

Mixup is a highly successful technique to improve generalization of neural networks by augmenting the training data with combinations of random pairs. Selective mixup is a family of methods that apply mixup to specific pairs, e.g. only…

Machine Learning · Computer Science 2023-06-06 Damien Teney , Jindong Wang , Ehsan Abbasnejad