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Volumn 4, Issue 4, 2010, Pages 1722-1748

Reuse, recycle, reweigh: Combating influenza through efficient sequential Bayesian computation for massive data

Author keywords

Gibbs variable selection; Hierarchical Bayesian model; Importance sampling; Influenza A; Markov chain Monte Carlo; Massive data

Indexed keywords


EID: 84858068293     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/10-AOAS349     Document Type: Article
Times cited : (12)

References (56)
  • 1
    • 60849119522 scopus 로고    scopus 로고
    • Statistical genetics and statistical genomics: Where biology, epistemology, statistics, and computation collide
    • Allison, D. B., Visscher, P. M., Rosa, G. J. M. and Amos, C. I. (2009). Statistical genetics and statistical genomics: Where biology, epistemology, statistics, and computation collide. Comput. Statist. Data Anal. 53 1531-1534.
    • (2009) Comput. Statist. Data Anal , vol.53 , pp. 1531-1534
    • Allison, D.B.1    Visscher, P.M.2    Rosa, G.J.M.3    Amos, C.I.4
  • 3
    • 34147115328 scopus 로고    scopus 로고
    • A one-pass sequential Monte Carlo method for Bayesian analysis of massive datasets
    • Balakrishnan, S. and Madigan, D. (2006). A one-pass sequential Monte Carlo method for Bayesian analysis of massive datasets. Bayesian Anal. 1 345-362.
    • (2006) Bayesian Anal , vol.1 , pp. 345-362
    • Balakrishnan, S.1    Madigan, D.2
  • 5
    • 65949086576 scopus 로고    scopus 로고
    • Swine flu goes global
    • Butler, D. (2009). Swine flu goes global. Nature 458 1082-1083.
    • (2009) Nature , vol.458 , pp. 1082-1083
    • Butler, D.1
  • 6
    • 0343171028 scopus 로고
    • Estimation of a multivariate density
    • Cacoullos, T. (1964). Estimation of a multivariate density. Ann. Inst. Statist. Math. 18 179-189.
    • (1964) Ann. Inst. Statist. Math , vol.18 , pp. 179-189
    • Cacoullos, T.1
  • 7
    • 44649107771 scopus 로고    scopus 로고
    • An overview of existing methods and recent advances in sequential Monte Carlo
    • Cappé, O., Godsill, S. J. and Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. IEEE Proceedings 95 899-924.
    • (2007) IEEE Proceedings , vol.95 , pp. 899-924
    • Cappé, O.1    Godsill, S.J.2    Moulines, E.3
  • 8
    • 0026516696 scopus 로고
    • Meta-analysis for 2×2 tables: A Bayesian approach
    • Carlin, J. B. (1992). Meta-analysis for 2×2 tables: A Bayesian approach. Stat. Med. 11 141-158.
    • (1992) Stat. Med , vol.11 , pp. 141-158
    • Carlin, J.B.1
  • 9
    • 2242491935 scopus 로고    scopus 로고
    • Computational and inferential difficulties with mixture posterior distributions
    • Celeux, G., Hurn, M. and Robert, C. P. (2000). Computational and inferential difficulties with mixture posterior distributions. J. Amer. Statist. Assoc. 95 957-970.
    • (2000) J. Amer. Statist. Assoc , vol.95 , pp. 957-970
    • Celeux, G.1    Hurn, M.2    Robert, C.P.3
  • 10
    • 21844506388 scopus 로고
    • Importance-weighted marginal Bayesian posterior density estimation
    • Chen, M. H. (1994). Importance-weighted marginal Bayesian posterior density estimation. J. Amer. Statist. Assoc. 89 818-824.
    • (1994) J. Amer. Statist. Assoc , vol.89 , pp. 818-824
    • Chen, M.H.1
  • 11
    • 0346307339 scopus 로고    scopus 로고
    • Probability density function estimation using gamma kernels
    • Chen, S. X. (2000). Probability density function estimation using gamma kernels. Ann. Inst. Statist. Math. 52 471-480.
    • (2000) Ann. Inst. Statist. Math , vol.52 , pp. 471-480
    • Chen, S.X.1
  • 12
    • 0012338718 scopus 로고    scopus 로고
    • A sequential particle filter method for static models
    • Chopin, N. (2002). A sequential particle filter method for static models. Biometrika 89 539-552.
    • (2002) Biometrika , vol.89 , pp. 539-552
    • Chopin, N.1
  • 13
    • 33751517736 scopus 로고    scopus 로고
    • Antiviral agents active against influenza A viruses
    • Clercq, E. D. (2006). Antiviral agents active against influenza A viruses. Nature Reviews Drug Discovery 5 1015-1025.
    • (2006) Nature Reviews Drug Discovery , vol.5 , pp. 1015-1025
    • Clercq, E.D.1
  • 15
    • 0041696122 scopus 로고    scopus 로고
    • On Bayesian model and variable selection using MCMC
    • Dellaportas, P., Forster, J. J. and Ntzoufras, I. (2002). On Bayesian model and variable selection using MCMC. Statist. Comput. 12 27-36.
    • (2002) Statist. Comput , vol.12 , pp. 27-36
    • Dellaportas, P.1    Forster, J.J.2    Ntzoufras, I.3
  • 16
    • 0008802221 scopus 로고    scopus 로고
    • An introduction to sequential Monte Carlo methods
    • In, (A. Doucet, N. de Freitas and N. Gordon, eds.) Springer, New York
    • Doucet, A., de Freitas, N. and Gordon, N. (2001). An introduction to sequential Monte Carlo methods. In Sequential Monte Carlo Methods in Practice (A. Doucet, N. de Freitas and N. Gordon, eds.) 3-13. Springer, New York.
    • (2001) Sequential Monte Carlo Methods in Practice , pp. 3-13
    • Doucet, A.1    de Freitas, N.2    Gordon, N.3
  • 17
    • 0036021407 scopus 로고    scopus 로고
    • Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data
    • Drummond, A. J., Nicholls, G. K., Rodrigo, A. G. and Solomon, W. (2002). Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data. Genetics 161 1307-1320.
    • (2002) Genetics , vol.161 , pp. 1307-1320
    • Drummond, A.J.1    Nicholls, G.K.2    Rodrigo, A.G.3    Solomon, W.4
  • 18
    • 17744381574 scopus 로고    scopus 로고
    • Bayesian coalescent inference of past population dynamics from molecular sequences
    • Drummond, A. J., Rambaut, A., Shapiro, B. and Pybus, O. G. (2005). Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol. 22 1185-1192.
    • (2005) Mol. Biol. Evol , vol.22 , pp. 1185-1192
    • Drummond, A.J.1    Rambaut, A.2    Shapiro, B.3    Pybus, O.G.4
  • 19
    • 84860575908 scopus 로고
    • Why isn't everyone a Bayesian?
    • Efron, B. (1986). Why isn't everyone a Bayesian? Amer. Statist. 40 1-11.
    • (1986) Amer. Statist , vol.40 , pp. 1-11
    • Efron, B.1
  • 20
    • 0000129805 scopus 로고
    • Stein's paradox in statistics
    • Efron, B. and Morris, C. (1977). Stein's paradox in statistics. Scientific American 236 119-127.
    • (1977) Scientific American , vol.236 , pp. 119-127
    • Efron, B.1    Morris, C.2
  • 21
    • 19544372097 scopus 로고    scopus 로고
    • Race against time
    • Fauci, A. S. (2005). Race against time. Nature 435 423-424.
    • (2005) Nature , vol.435 , pp. 423-424
    • Fauci, A.S.1
  • 23
    • 0001032163 scopus 로고
    • Evaluating the accuracy of sampling-based approaches to calculating posterior moments
    • In, (J. M. Bernado, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds.) Oxford Univ. Press, New York
    • Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bayesian Statistics, vol. 4 (J. M. Bernado, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds.) 169-193. Oxford Univ. Press, New York.
    • (1992) Bayesian Statistics, vol. 4 , pp. 169-193
    • Geweke, J.1
  • 31
    • 0030327756 scopus 로고    scopus 로고
    • The selection of prior distributions by formal rules
    • Kass, R. E. and Wasserman, L. (1996). The selection of prior distributions by formal rules. J. Amer. Statist. Assoc. 91 1343-1370.
    • (1996) J. Amer. Statist. Assoc , vol.91 , pp. 1343-1370
    • Kass, R.E.1    Wasserman, L.2
  • 32
  • 33
    • 0842287651 scopus 로고    scopus 로고
    • The burgeoning field of statistical phylogeography
    • Knowles, L. L. (2004). The burgeoning field of statistical phylogeography. J. Evol. Biol. 17 1-10.
    • (2004) J. Evol. Biol , vol.17 , pp. 1-10
    • Knowles, L.L.1
  • 35
    • 33645895554 scopus 로고    scopus 로고
    • The challenge of subgroup analysis-reporting without distorting
    • Lagakos, S. W. (2006). The challenge of subgroup analysis-reporting without distorting. New England Journal of Medicine 354 1667-1669.
    • (2006) New England Journal of Medicine , vol.354 , pp. 1667-1669
    • Lagakos, S.W.1
  • 37
    • 34548378755 scopus 로고    scopus 로고
    • A hierarchical semiparametric regression model for combining HIV-1 phylogenetic analysis using iterative reweighting algorithm
    • Liang, L. J. and Weiss, R. E. (2007). A hierarchical semiparametric regression model for combining HIV-1 phylogenetic analysis using iterative reweighting algorithm. Biometrics 63 733-741.
    • (2007) Biometrics , vol.63 , pp. 733-741
    • Liang, L.J.1    Weiss, R.E.2
  • 38
    • 70349878973 scopus 로고    scopus 로고
    • Improving phylogenetic analyses by incorporating additional information from genetic sequence databases
    • Liang, L. J., Weiss, R. E., Redelings, B. and Suchard, M. A. (2009). Improving phylogenetic analyses by incorporating additional information from genetic sequence databases. Bioinformatics 25 2530-2536.
    • (2009) Bioinformatics , vol.25 , pp. 2530-2536
    • Liang, L.J.1    Weiss, R.E.2    Redelings, B.3    Suchard, M.A.4
  • 39
    • 45849122039 scopus 로고    scopus 로고
    • Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics
    • Minin, V. M., Bloomquist, E. W. and Suchard, M. A. (2008). Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. Mol. Biol. Evol. 25 1459-1471.
    • (2008) Mol. Biol. Evol , vol.25 , pp. 1459-1471
    • Minin, V.M.1    Bloomquist, E.W.2    Suchard, M.A.3
  • 40
    • 33847044691 scopus 로고    scopus 로고
    • The evolution of the epidemic influenza
    • Nelson, M. I. and Holmes, E. C. (2007). The evolution of the epidemic influenza. Nature Reviews Genetics 8 196-204.
    • (2007) Nature Reviews Genetics , vol.8 , pp. 196-204
    • Nelson, M.I.1    Holmes, E.C.2
  • 42
    • 0037527978 scopus 로고    scopus 로고
    • A sequential Monte Carlo method for Bayesian analysis of massive datasets
    • Ridgeway, G. and Madigan, D. (2003). A sequential Monte Carlo method for Bayesian analysis of massive datasets. Data Mining and Knowledge Discovery 7 301-319.
    • (2003) Data Mining and Knowledge Discovery , vol.7 , pp. 301-319
    • Ridgeway, G.1    Madigan, D.2
  • 44
    • 0000458272 scopus 로고
    • Using the SIR algorithm to simulate posterior distributions
    • In, (J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.) Oxford Univ. Press
    • Rubin, D. B. (1988). Using the SIR algorithm to simulate posterior distributions. In Bayesian Statistics 3 (J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.) 395-402. Oxford Univ. Press.
    • (1988) Bayesian Statistics 3 , pp. 395-402
    • Rubin, D.B.1
  • 45
    • 59049093098 scopus 로고    scopus 로고
    • The influenza virus enigma
    • Salomon, R. and Webster, R. G. (2009). The influenza virus enigma. Cell 136 402-410.
    • (2009) Cell , vol.136 , pp. 402-410
    • Salomon, R.1    Webster, R.G.2
  • 48
    • 65649118549 scopus 로고    scopus 로고
    • Many-core algorithms for statistical phylogenetics
    • Suchard, M. A. and Rambaut, A. (2009). Many-core algorithms for statistical phylogenetics. Bioinformatics 25 1370-1376.
    • (2009) Bioinformatics , vol.25 , pp. 1370-1376
    • Suchard, M.A.1    Rambaut, A.2
  • 49
    • 0242404116 scopus 로고    scopus 로고
    • Hierarchical phylogenetic models for analyzing multipartite sequence data
    • Suchard, M. A., Kitchen, C. M. R., Sinsheimer, J. S. and Weiss, R. E. (2003). Hierarchical phylogenetic models for analyzing multipartite sequence data. Systematic Biology 52 649-664.
    • (2003) Systematic Biology , vol.52 , pp. 649-664
    • Suchard, M.A.1    Kitchen, C.M.R.2    Sinsheimer, J.S.3    Weiss, R.E.4
  • 50
    • 77955516707 scopus 로고    scopus 로고
    • Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures
    • Suchard, M. A., Wang, Q., Chan, C., Frelinger, J., Cron, A. and West, M. (2010). Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures. J. Comput. Graph. Statist. 19 419-438.
    • (2010) J. Comput. Graph. Statist , vol.19 , pp. 419-438
    • Suchard, M.A.1    Wang, Q.2    Chan, C.3    Frelinger, J.4    Cron, A.5    West, M.6
  • 52
    • 0037098198 scopus 로고    scopus 로고
    • Bayesian random effects meta-analysis of trials with binary outcomes: Methods for the absolute risk difference and relative risk scales
    • Warn, D. E., Thompson, S. G. and Spiegelhalter, D. J. (2002). Bayesian random effects meta-analysis of trials with binary outcomes: Methods for the absolute risk difference and relative risk scales. Stat. Med. 21 1601-1623.
    • (2002) Stat. Med , vol.21 , pp. 1601-1623
    • Warn, D.E.1    Thompson, S.G.2    Spiegelhalter, D.J.3
  • 53
    • 0037337361 scopus 로고    scopus 로고
    • The world is teetering on the edge of a pandemic that could kill a large fraction of the human population
    • Webster, R. G. and Walker, E. J. (2003). The world is teetering on the edge of a pandemic that could kill a large fraction of the human population. Amer. Sci. 91 122.
    • (2003) Amer. Sci , vol.91 , pp. 122
    • Webster, R.G.1    Walker, E.J.2
  • 54
    • 0001771498 scopus 로고
    • Evolution in Mendelian populations
    • Wright, S. (1931). Evolution in Mendelian populations. Genetics 16 97-159.
    • (1931) Genetics , vol.16 , pp. 97-159
    • Wright, S.1
  • 56
    • 12444337674 scopus 로고    scopus 로고
    • Model parameterization, prior distributions, and the general time-reversible model in Bayesian phylogenetics
    • Zwickl, D. J. and Holder, M. T. (2004). Model parameterization, prior distributions, and the general time-reversible model in Bayesian phylogenetics. Systematic Biology 53 877-888.
    • (2004) Systematic Biology , vol.53 , pp. 877-888
    • Zwickl, D.J.1    Holder, M.T.2


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