A novel approach for choosing summary statistics in approximate Bayesian computation.
about
Viral phylodynamicsModeling tuberculosis dynamics, detection and control in cattle herdsOrigin and demographic history of the endemic Taiwan spruce (Picea morrisonicola)Robust demographic inference from genomic and SNP dataABC inference of multi-population divergence with admixture from unphased population genomic data.Amount of information needed for model choice in Approximate Bayesian Computation.Human-facilitated metapopulation dynamics in an emerging pest species, Cimex lectulariusUsing ABC and microsatellite data to detect multiple introductions of invasive species from a single source.Deep Learning for Population Genetic Inference.Likelihood-Free Inference in High-Dimensional Models.The summary-likelihood method and its implementation in the Infusion package.Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.Genomic Signatures of Selective Pressures and Introgression from Archaic Hominins at Human Innate Immunity GenesImportance of incomplete lineage sorting and introgression in the origin of shared genetic variation between two closely related pines with overlapping distributions.Approximate Bayesian inference for complex ecosystems.Evolutionary processes driving spatial patterns of intraspecific genetic diversity in river ecosystems.Inconvenient truths in population and speciation genetics point towards a future beyond allele frequencies.Inferring past demographic changes from contemporary genetic data: A simulation-based evaluation of the ABC methods implemented in diyabc.Sequence diversity patterns suggesting balancing selection in partially sex-linked genes of the plant Silene latifolia are not generated by demographic history or gene flow.Fundamentals and Recent Developments in Approximate Bayesian Computation.Reliable ABC model choice via random forests.Inferring the geographic mode of speciation by contrasting autosomal and sex-linked genetic diversity.Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates.Supervised Machine Learning for Population Genetics: A New Paradigm.Evaluating the Neolithic Expansion at Both Shores of the Mediterranean Sea.The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: the effects of sequencing techniques and sampling strategiesThe impact of experimental design choices on parameter inference for models of growing cell colonies
P2860
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P2860
A novel approach for choosing summary statistics in approximate Bayesian computation.
description
2012 nî lūn-bûn
@nan
2012 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
A novel approach for choosing summary statistics in approximate Bayesian computation.
@ast
A novel approach for choosing summary statistics in approximate Bayesian computation.
@en
A novel approach for choosing summary statistics in approximate Bayesian computation.
@nl
type
label
A novel approach for choosing summary statistics in approximate Bayesian computation.
@ast
A novel approach for choosing summary statistics in approximate Bayesian computation.
@en
A novel approach for choosing summary statistics in approximate Bayesian computation.
@nl
prefLabel
A novel approach for choosing summary statistics in approximate Bayesian computation.
@ast
A novel approach for choosing summary statistics in approximate Bayesian computation.
@en
A novel approach for choosing summary statistics in approximate Bayesian computation.
@nl
P2093
P2860
P1433
P1476
A novel approach for choosing summary statistics in approximate Bayesian computation.
@en
P2093
Andreas Futschik
Mark A Beaumont
Simon Aeschbacher
P2860
P304
P356
10.1534/GENETICS.112.143164
P407
P577
2012-09-07T00:00:00Z