Probability, statistics, and computational science

Methods in Molecular Biology

Volumn 855, Issue , 2012, Pages 77-110

  Beerenwinkel, Niko ^a   Siebourg, Juliane ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Bayesian inference; Bayesian networks; Dynamic programming; Expectation maximization algorithm; Hidden Markov models; Markov chains; Maximum likelihood; Statistical models

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGY; INSTRUMENTATION; PROBABILITY; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; MARKOV CHAINS; MODELS, STATISTICAL; PROBABILITY;

EID: 84859991597     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-61779-582-4_3     Document Type: Article

References (30)

1. Ewens,W. J. and Grant, G. R. (2005) Statistical methods in bioinformatics: an introduction. Springer, New York, NY.
2. Deonier, R. C., Tavaré, S., and Waterman, M. S. (2005) Computational genome analysis: an introduction. Springer, New York, NY.
3. Davison, A. C. (2009) Statistical models. Cambridge University Press, Cambridge, UK.
4. Ross, S. M. (2007) Introduction to probabilitymodels. Academic Press.
5. Hardy, G.H. (1908) Mendelian proportions in a mixed population. Science, 28, 49.
6. Weinberg, W. (1908) € Uber den Nachweis derVererbung beim Menschen. Jahreshefte des Vereins f€ur vaterl€andische Naturkunde in W€urttemberg, 64, 368-382.
7. Pachter, L. and Sturmfels, B. (eds.) (2005) Algebraic statistics for computational biology.
8. Casella, G. and Berger, R. L. (2002) Statistical inference. Thomson Learning, Pacific Grove, CA.
9. Efron, B. and Tibshirani, R. (1993) An introduction to the bootstrap. Chapman&Hall/CRC, New York, NY.
10. Gelman, A., Carlin, J. B., Stern, H. S., andRubin, D. B. (2003) Bayesian data analysis,second edition. Chapman & Hall/CRC, BocaRaton, Fla.
11. Dempster, A. P., Laird, N. M., Rubin, D. B., et al. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39, 1-38.
12. Norris, J. R. (1998) Markov chains. Cambridge University Press.
13. Wright, S. (1990) Evolution in Mendelianpopulations. Bulletin of Mathematical Biology, 52, 241-295.
14. Fisher, R. A. (1930) The genetical theory of natural selection. Clarendon Press, Oxford, UK.
15. Jukes, T. H. andCantor, C. R. (1969) Evolution of protein molecules. Mammalian protein metabolism, 3, 21-132.
16. Rabiner, L. R. (1989) A tutorial on HMM and selected applications in speech recognition. Proceedings of the IEEE, 77, 257-286.
17. Durbin, R. (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge, UK.
18. Viterbi, A. (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13, 260-269.
19. Baum, L. E. (1972) An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequalities, 3, 1-8.
20. Bishop, C. M. (2006) Pattern recognition and machine learning. Springer, New York.
21. Husmeier, D., Dybowski, R., and Roberts, S. (2005) Probabilistic modeling in bioinformatics and medical informatics. Springer, New York.
22. Koller, D. and Friedman, N. (2009) Probabilistic graphical models: principles and techniques. The MIT Press, Cambridge, MA.
23. Jordan, M. I. (1998) Learning in graphical models. Kluwer Academic Publishers, Cambridge, MA.
24. Schwarz, G. (1978) Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
25. Neal, R. M. (1993) Probabilistic inference using Markov Chain Monte Carlo methods. Intelligence, 62, 144.
26. Hastings, W. K. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97.
27. Geman, S. and Geman, D. (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741. (Pubitemid 15453722)
28. Felsenstein, J. (2004) Inferring phylogenies. Sinauer Associates, Sunderland, MA.
29. Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution, 17, 368-376. (Pubitemid 11016207)
30. Siepel, A. and Haussler, D. (2005) Phylogenetic hidden Markov models. Statistical Methods in Molecular Evolution, pp. 325-351 110 N. Beerenwinkel and J. Siebourg