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Tree-guided Bayesian inference of population structures
Yu Zhang
Statistics
Institute for Computational and Data Sciences (ICDS)
Huck Institutes of the Life Sciences
Penn State Cancer Institute
Cancer Institute, Mechanisms of Carcinogenesis
Research output
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Contribution to journal
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Article
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peer-review
6
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Scopus citations
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Dive into the research topics of 'Tree-guided Bayesian inference of population structures'. Together they form a unique fingerprint.
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Keyphrases
Single nucleotide Polymorphism
100%
Population Structure
100%
Bayesian Inference
100%
Population number
100%
Genetic Markers
50%
Convergence Rate
50%
Random Variables
50%
Genetic Data
50%
Challenging Tasks
50%
Unlinked
50%
Existing Algorithms
50%
Population Stratification
50%
Partitioning Method
50%
Data Sampling
50%
Bayesian Methods
50%
Improved Convergence
50%
Estimation Procedures
50%
Tree-based Models
50%
Nuisance Parameters
50%
Mathematics
Bayesian
100%
Convergence Rate
100%
Bayesian Inference
100%
Nuisance Parameter
100%
Partition Method
100%
Random Variable
100%
Population Correlation
100%
Computer Science
Unknown Parameter
100%
Estimation Procedure
100%
Convergence Rate
100%
Random Variable
100%
Nuisance Parameter
100%
Biochemistry, Genetics and Molecular Biology
Population Structure
100%
Single-Nucleotide Polymorphism
66%
Genetics
33%
Genetic Marker
33%