Population Markov Chain Monte Carlo (original) (raw)
References
Back, T. (1996). Evolutionary algorithms in theory and practice. New York: Oxford University Press. Google Scholar
Beinlich, I. A., &; Suermondt, H. J., et al. (1989). The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proceedings of the Second European Conference on Artificial Intelligence in Medicine.
Cooper, G. F., &; Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347. Google Scholar
Davis, T. E., &; Principe, J. C. (1993). A Markov chain framework for the simple genetic algorithm. Evolutionary Computation, 1:3, 269-288. Google Scholar
DeJong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. In Computer and communication sciences (p. 256). Ann Arbor, MI: University of Michigan. Google Scholar
DeJong, K. A., &; Spears, W. M. (1990). An analysis of the interacting roles of population size and crossover in genetic algorithms. In Proceedings of the First International Conference on Parallel Problem Solving from Nature, Dortmund, Germany.
Feller, W. (1968). An introduction to probability theory and its applications. New York: Wiley. Google Scholar
Fogel, L. J. (1991). System identification through simulated evolution: A machine learning approach to modeling. Needham Heights: Ginn Press. Google Scholar
Friedman, N. (1998a). The Bayesian structural EM algorithm. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufmann. Google Scholar
Friedman, N. (1998b). Learning belief networks in the presence of missing values and hidden variables. In Fourteenth International Conference on Machine Learning (ICML-97). San Mateo, CA: Morgan Kaufmann. Google Scholar
Gelman, A., Carlin, J., Stern, H., &; Rubin, D. (1995). Bayesian data analysis. London: Chapman and Hall. Google Scholar
Gelman, A., &; Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457-472. Google Scholar
Geyer, C. J. (1991). Markov chain Monte Carlo maximum likelihood. In E. D. Keramidas (Ed.), Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface (pp. 156-163). Fairfax Station: Interface Foundation. Google Scholar
Gilks, W. R., Richardson, S., &; Spiegelhalter, D. (Eds) (1996). Markov chain Monte Carlo in practice. London: Chapman and Hall. Google Scholar
Gilks, W. R., Roberts, G. O., &; Sahu, S. K. (1998). Adaptive Markov chain Monte Carlo through regeneration. Journal of the American Statistical Association, 93, 1045-1054. Google Scholar
Goldberg, D. E., &; Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In G. J. E. Rawlins (Ed.), Foundations of genetic algorithms (Vol. 1, pp. 69-93). San Mateo, CA: Morgan Kaufmann. Google Scholar
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57:1, 97-109. Google Scholar
Heckerman, D. (1996). A tutorial on learning with Bayesian networks. Redmond WA: Microsoft. Google Scholar
Heckerman, D., Geiger, D., et al. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20, 197-243. Google Scholar
Hoeting, J., Madigan, D., Raftery, A., &; Volinsky, C. (1996). Bayesian model averaging, Technical Report #335, Department of Statistics, University of Washingtion, Seattle, WA. Google Scholar
Holland, J. H. (1995). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press. Google Scholar
Holmes, C. C., &; Mallick, B. K. (1998). Parallel Markov chain Monte Carlo sampling: An evolutionary based approach. London: Imperial College. Google Scholar
Jensen, F. V. (2001). Bayesian networks and decision graphs. New York: Springer. Google Scholar
Kass, R., &; Raftery, A. (1995). Bayes factors. Journal of the American Statistical Association, 90:430, 773-795. Google Scholar
Kitano, H. (1990). Designing neural networks using genetic algorithms with graph generation systems. Complex Systems, 4, 461-476. Google Scholar
Larrañaga, P., &; Poza, M., et al. (1996). Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Journal on Pattern Analysis and Machine Intelligence, 18:9, 912-926. Google Scholar
Lauritzen, S. (1995). The EM algorithm for graphical association models with missing data. Computational Statistics &; Data Analysis, 19, 191-201. Google Scholar
Lauritzen, S. (1996). Graphical models. Oxford: Oxford Science Publications. Google Scholar
Little, R., &; Rubin, D. (1987). Statistical analysis with missing data. New York: John Wiley &; Sons. Google Scholar
Madigan, D., Raftery, A. E., et al. (1994). Strategies for graphical model selection. In P. Cheeseman, &; W. Oldford (Eds.), Selecting models from data: Artificial intelligence and statistics IV (pp. 91-100). Berlin: Springer-Verlag. Google Scholar
Madigan, D.,&; York, J. (1993). Bayesian graphical models for discrete data. Technical Report no. 259, Department of Statistics, University of Washingtion, Seattle, WA. Google Scholar
Metropolis, N., Rosenbluth, A. W., et al. (1953). Equations of state calculation by fast computing machines. Journal of Chemical Physics, 21, 1087-1092. Google Scholar
Neal, R. (1993). Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco: Morgan Kaufmann. Google Scholar
Roberts, G. O. (1996). The Gibbs sampler and Metropolis-Hastings algorithm. In W. R. Gilks, S. Richardson, &; D. Spiegelhalter (Eds.), Markov chain Monte Carlo in practice. London: Chapman &; Hall. Google Scholar
Schwefel, H.-P. (1995). Evolution and optimum seeking. New York: John Wiley &; Sons. Google Scholar
Sewell, W., &; Shah, V. (1968). Social class, parental encouragement, and educational aspirations. American Journal of Sociology, 73, 559-572. Google Scholar
Spears, W. M. (1994). Simple subpopulation schemes. In Proceedings of the Third Annual Conference on Evolutionary Programming. San Diego: World Scientific. Google Scholar
Spiegelhalter, D., &; Lauritzen, S. (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks, 20, 279-605. Google Scholar
Syswerda, G. (1989). Uniform crossover in genetic algorithms. In Proceedings of the 3rd International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann. Google Scholar
Whittaker, J. (1990). Graphical models in applied multivariate statistics. Chichester: John Wiley &; Sons. Google Scholar