Bayesian computation and model selection without likelihoods

Genetics

Volumn 184, Issue 1, 2010, Pages 243-252

  Leuenberger, Christoph ^a   Wegmann, Daniel ^b  

^a UNIVERSITY OF FRIBOURG   (Switzerland)

^b UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; CHIMPANZEE; CONCEPTUAL FRAMEWORK; METHODOLOGY; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; REGRESSION ANALYSIS; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ANIMALS; BAYES THEOREM; GENETICS, POPULATION; HUMANS; LIKELIHOOD FUNCTIONS; LINEAR MODELS; MODELS, GENETIC; PAN TROGLODYTES;

PAN; PAN TROGLODYTES VERUS;

EID: 74249110200     PISSN: 00166731     EISSN: 00166731     Source Type: Journal    
DOI: 10.1534/genetics.109.109058     Document Type: Article

References (22)

1. BEAUMONT, M., 2007 Simulations, Genetics, and Human Prehistory - A Focus on Islands. McDonald Institute Monographs, University of Cambridge, Cambridge, UK.
2. BEAUMONT, M., W. ZHANG and D. BALDING, 2002 Approximate Bayesian computation in population genetics. Genetics 162: 2025-2035.
3. BEAUMONT, M., R. C. CORNUET and J.-M. MARIN, 2009 Adaptive approximate Bayesian computation. Biometrika (in press).
4. BECQUET, C., and M. PRZEWORSKI, 2007 A new approach to estimate parameters of speciation models with application to apes. Genome Res. 17: 1505-1519.
5. BECQUET, C., N. PATTERSON, A. STONE, M. PRZEWORSKI and D. REICH, 2007 Genetic structure of chimpanzee populations. Genome Res. 17: 1505-1519.
6. BLUM, M., and O. FRANCOIS, 2009 Non-linear regression models for approximate Bayesian computation. Stat. Comput. (in press).
7. ESTOUP, A., M. BEAUMONT, F. SENNEDOT, C. MORITZ and J. M. CORNUET, 2004 Genetic analysis of complex demographic scenarios: spatially expanding populations of the cane toad, Bufo marinus. Evolution 58: 2021-2036.
8. EXCOFFIER, L., G. LAVAL and S. SCHNEIDER, 2005 Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol. Bioinform. Online 1: 47-50.
9. GREENE, W., 2003 Econometric Analysis, Ed. 5. Pearson Education, Upper Saddle River, NJ.
10. HAMILTON, G., M. STONEKING and L. EXCOFFIER, 2006 Molecular analysis reveals tighter social regulation of immigration in patrilocal populations than in matrilocal populations. Proc. Natl. Acad. Sci. USA 102: 7476-7480.
11. LAVAL, G., and L. EXCOFFIER, 2004 Simcoal 2.0: a program to simulate genomic diversity over large recombining regions in a subdivided population with a complex history. Bioinformatics 20: 2485-2487.
12. LINDLEY, D., and A. SMITH, 1972 Bayes estimates for the linear model. J. R. Stat. Soc. B 34: 1-44.
13. MARJORAM, P., and S. TAVARÉ, 2006 Modern computational approaches for analysing molecular genetic variation data. Nat. Rev. Genet. 10: 759-770.
14. MARJORAM, P., J. MOLITOR, V. PLAGNOL and S. TAVARÉ, 2003 Markov chain Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. USA 100: 15324-15328.
15. SISSON, S., Y. FAN and M. TANAKA, 2007 Sequential Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. USA 104: 1760-1765.
16. TALLMON, D. A., G. LUIKART and M. A. BEAUMONT, 2004 Comparative evaluation of a new effective population size estimator based on approximate Bayesian computation. Genetics 167: 977-988.
17. TAVARÉ, S., D. BALDING, R. GRIFFITHS and P. DONNELLY, 1997 Inferring coalescence times from DNA sequence data. Genetics 145: 505-518.
18. WATTERSON, G., 1975 Number of segregating sites in genetic models without recombination. Theor. Popul. Biol. 7: 256-276.
19. WEGMANN, D., C. LEUENBERGER AND L. EXCOFFIER, 2009 Efficient approximate Bayesian computation coupled Markov chain Monte Carlo without likelihood. Genetics 182: 1207-1218.
20. WEISS, G., and A. VON HAESELER, 1998 Inference of population history using a likelihood approach. Genetics 149: 1539-1546.
21. WON, Y., and J. HEY, 2005 Divergence population genetics of chimpanzees. Mol. Biol. Evol. 22: 297-307.
22. ZELLNER, A., 1971 An Introduction to Bayesian Inference in Econometrics. Wiley, New York.