Computational aspects of DNA mixture analysis

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Computational aspects of DNA mixture analysis. / Graversen, Therese; Lauritzen, Steffen L.

In: Statistics and Computing, Vol. 25, No. 3, 2015, p. 527-541.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Graversen, T & Lauritzen, SL 2015, 'Computational aspects of DNA mixture analysis', Statistics and Computing, vol. 25, no. 3, pp. 527-541. https://doi.org/10.1007/s11222-014-9451-7

APA

Graversen, T., & Lauritzen, S. L. (2015). Computational aspects of DNA mixture analysis. Statistics and Computing, 25(3), 527-541. https://doi.org/10.1007/s11222-014-9451-7

Vancouver

Graversen T, Lauritzen SL. Computational aspects of DNA mixture analysis. Statistics and Computing. 2015;25(3):527-541. https://doi.org/10.1007/s11222-014-9451-7

Author

Graversen, Therese ; Lauritzen, Steffen L. / Computational aspects of DNA mixture analysis. In: Statistics and Computing. 2015 ; Vol. 25, No. 3. pp. 527-541.

Bibtex

@article{960217216dc74c3197702a38d3172c62,
title = "Computational aspects of DNA mixture analysis",
abstract = "Statistical analysis of DNA mixtures for forensic identification is known to pose computational challenges due to the enormous state space of possible DNA profiles. We describe a general method for computing the expectation of a product of discrete random variables using auxiliary variables and probability propagation in a Bayesian network. We propose a Bayesian network representation for genotypes, allowing computations to be performed locally involving only a few alleles at each step. Exploiting appropriate auxiliary variables in combination with this representation allows efficient computation of the likelihood function and prediction of genotypes of unknown contributors. Importantly, we exploit the computational structure to introduce a novel set of diagnostic tools for assessing the adequacy of the model for describing a particular dataset.",
author = "Therese Graversen and Lauritzen, {Steffen L.}",
year = "2015",
doi = "10.1007/s11222-014-9451-7",
language = "English",
volume = "25",
pages = "527--541",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - Computational aspects of DNA mixture analysis

AU - Graversen, Therese

AU - Lauritzen, Steffen L.

PY - 2015

Y1 - 2015

N2 - Statistical analysis of DNA mixtures for forensic identification is known to pose computational challenges due to the enormous state space of possible DNA profiles. We describe a general method for computing the expectation of a product of discrete random variables using auxiliary variables and probability propagation in a Bayesian network. We propose a Bayesian network representation for genotypes, allowing computations to be performed locally involving only a few alleles at each step. Exploiting appropriate auxiliary variables in combination with this representation allows efficient computation of the likelihood function and prediction of genotypes of unknown contributors. Importantly, we exploit the computational structure to introduce a novel set of diagnostic tools for assessing the adequacy of the model for describing a particular dataset.

AB - Statistical analysis of DNA mixtures for forensic identification is known to pose computational challenges due to the enormous state space of possible DNA profiles. We describe a general method for computing the expectation of a product of discrete random variables using auxiliary variables and probability propagation in a Bayesian network. We propose a Bayesian network representation for genotypes, allowing computations to be performed locally involving only a few alleles at each step. Exploiting appropriate auxiliary variables in combination with this representation allows efficient computation of the likelihood function and prediction of genotypes of unknown contributors. Importantly, we exploit the computational structure to introduce a novel set of diagnostic tools for assessing the adequacy of the model for describing a particular dataset.

U2 - 10.1007/s11222-014-9451-7

DO - 10.1007/s11222-014-9451-7

M3 - Journal article

VL - 25

SP - 527

EP - 541

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 3

ER -

ID: 128111876