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Institution

University of Warwick

EducationCoventry, Warwickshire, United Kingdom
About: University of Warwick is a education organization based out in Coventry, Warwickshire, United Kingdom. It is known for research contribution in the topics: Population & White dwarf. The organization has 26212 authors who have published 77127 publications receiving 2666552 citations. The organization is also known as: Warwick University & The University of Warwick.


Papers
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Book
15 Apr 2015
TL;DR: Tropical islands Building blocks Tropical varieties Tropical rain forest Tropical garden Toric connections Bibliography Index Bibliography as mentioned in this paper, Section 5.1.1] and Bibliography 2.2.
Abstract: Tropical islands Building blocks Tropical varieties Tropical rain forest Tropical garden Toric connections Bibliography Index

552 citations

Journal ArticleDOI
TL;DR: In this paper, the authors argue that the ability to predict and manage the function of these highly complex, dynamically changing communities is limited, and that close coordination of experimental data collection and method development with mathematical model building is needed to achieve significant progress in understanding of microbial dynamics and function.
Abstract: The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

552 citations

Journal ArticleDOI
TL;DR: In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities, and when testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted theinferences most accurately.
Abstract: The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in 4 experimental studies. Participants substantially improved their inferences through feedback. In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.

551 citations

Journal ArticleDOI
TL;DR: In this paper, the authors studied the complexity of finding the values and optimal strategies of mean payoff games on graphs, a family of perfect information games introduced by Ehrenfeucht and Mycielski and considered by Gurvich, Karzanov and Khachiyan.

551 citations

Journal ArticleDOI
TL;DR: The authors investigated whether dropout in the Avon Longitudinal study of parents and children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders.
Abstract: Background Participant drop-out occurs in all longitudinal studies, and if systematic, may lead to selection biases and erroneous conclusions being drawn from a study. Aims We investigated whether drop out in the Avon Longitudinal Study of Parents And Children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders. Method Teacher reports of disruptive behaviour among currently participating, previously participating and never participating children aged 8 years in the ALSPAC longitudinal study were collected. Data on family factors were obtained in pregnancy. Simulations were conducted to explain the impact of selective drop-out on the strength of prediction. Results Drop out from the ALSPAC cohort was systematic and children who dropped out were more likely to suffer from disruptive behaviour disorder. Systematic participant drop-out according to the family variables, however, did not alter the association between family factors obtained in pregnancy and disruptive behaviour disorder at 8 years of age. Conclusions Cohort studies are prone to selective drop-out and are likely to underestimate the prevalence of psychiatric disorder. This empirical study and the simulations confirm that the validity of regression models is only marginally affected despite range restrictions after selective drop-out.

550 citations


Authors

Showing all 26659 results

NameH-indexPapersCitations
David Miller2032573204840
Daniel R. Weinberger177879128450
Kay-Tee Khaw1741389138782
Joseph E. Stiglitz1641142152469
Edmund T. Rolls15361277928
Thomas J. Smith1401775113919
Tim Jones135131491422
Ian Ford13467885769
Paul Harrison133140080539
Sinead Farrington133142291099
Peter Hall132164085019
Paul Brennan132122172748
G. T. Jones13186475491
Peter Simmonds13182362953
Tim Martin12987882390
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023195
2022734
20214,816
20204,927
20194,602
20184,132