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Data from: A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies
负责人:
关键词:
winter severity;Bayesian;white-tailed deer;Migration;state-space;partially observed state;latent variable;1991-2006;Odocoileus virginianus
DOI:
doi:10.5061/dryad.4430n
摘要:
e observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each
Data from: Effects of complex life cycles on genetic diversity: cyclical parthenogenesis
负责人:
Stoeckel, Solenn
关键词:
Markov chains Cyclical parthenogenesis FIS distribution de Finetti diagrams individual-based simulations gametic phase disequilibrium
DOI:
doi:10.5061/dryad.r08vm
摘要:
ious clonal phase lengths. Using a Markov chain model of CP for a single locus and individual-based simulations for two loci, our analysis first demonstrates
Data from: Bridging inter- and intraspecific trait evolution with a hierarchical Bayesian approach
负责人:
关键词:
intraspecific variance;comparative phylogenetics;Macroevolution;Markov chain Monte Carlo;JIVE;hierarchical Bayesian model;niche breadth
DOI:
doi:10.5061/dryad.n4vp0
摘要:
of trait evolution by accounting for both trait mean and variance. Here, we present a model of phenotypic trait evolution using a hierarchical Bayesian approach tha
Data from: State-space modelling of the flight behaviour of a soaring bird provides new insights to migratory strategies
负责人:
Pirotta, Enrico
关键词:
3D states GPS-GSM telemetry hidden state model Markov chain Monte Carlo movement ecology raptor subsidised flight
DOI:
doi:10.5061/dryad.44v9r82
摘要:
1. Characterizing the spatiotemporal variation of animal behaviour can elucidate the way individuals interact with their environment and allocate
Data from: Marine subsidies change short-term foraging activity and habitat utilization of terrestrial lizards
负责人:
关键词:
subsidy;habitat use;Anolis sagrei
DOI:
doi:10.5061/dryad.bc0qk
摘要:
Markov chain models of perch height as a function of foraging state. These models suggest a “Synchronized-satiation Hypothesis,” whereby lizards respond synch
Data from: Bayesian methods for estimating GEBVs of threshold traits
负责人:
关键词:
Genomic selection;Bayesian methods;MCMC;threshold traits
DOI:
doi:10.5061/dryad.pp551
摘要:
e correspondingly termed BayesTA, BayesTB and BayesTC?. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo (MCMC) algorithm were derived
Data from: Aggressive behaviours, food deprivation and the foraging gene
负责人:
关键词:
behaviour;aggression;pleiotropy;drosophila melanogaster
DOI:
doi:10.5061/dryad.41pr0
摘要:
of offensive behaviour than sitters and s2, a sitter-like mutant on rover genetic background. With a Markov chain model, we estimated the rate of aggression
Data from: Full Bayesian comparative phylogeography from genomic data
负责人:
关键词:
Bayesian model choice;Neogene;Gekko crombota;Gekko mindorensis;Biogeography;Quaternary;Gekko rossi;Dirichlet-process prior;phylogeography
DOI:
doi:10.5061/dryad.4b3j2bj
摘要:
-averaged posterior via Markov chain Monte Carlo algorithms. Using simulations, we find that the new method is much more accurate and precise at estima
Data from: Bayes factors unmask highly variable information content, bias, and extreme influence in phylogenomic analyses
负责人:
关键词:
information content;Bayes factors;Bias;turtles;ultraconserved elements;amniotes;posterior probability;phylogenomics;Expressed Sequence Tags;Negative Constraints;ortholog
DOI:
doi:10.5061/dryad.8gm85
摘要:
as Markov chain Monte Carlo estimates of posterior probabilities). Bayes factors reveal important, previously hidden, differences across six “phylogenomic” data sets collected
Data from: Inferring heterogeneous evolutionary processes through time: from sequence substitution to phylogeography
负责人:
关键词:
phylogeography;phylogenetics;Epoch Model;BEAGLE;Bayesian inference;BEAST
DOI:
doi:10.5061/dryad.qp747
摘要:
in a Bayesian inference framework that offers great modeling flexibility in drawing inference about any discrete data type characterized as a continuous-time Markov chain

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