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Volumn 28, Issue , 2007, Pages 1-48

Cutset sampling for bayesian networks

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; NUMERICAL METHODS; SET THEORY;

EID: 34249088006     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2149     Document Type: Article
Times cited : (46)

References (70)
  • 1
    • 0032089922 scopus 로고    scopus 로고
    • Approximating maps for belief networks is NP-hard and other theorems
    • Abdelbar, A. M., & Hedetniemi, S. M. (1998). Approximating maps for belief networks is NP-hard and other theorems. Artificial Intelligence, 102, 21-38.
    • (1998) Artificial Intelligence , vol.102 , pp. 21-38
    • Abdelbar, A.M.1    Hedetniemi, S.M.2
  • 3
    • 0021787868 scopus 로고
    • Efficient algorithms for combinatorial problems on graphs with bounded decomposability - a survey
    • Arnborg, S. A. (1985). Efficient algorithms for combinatorial problems on graphs with bounded decomposability - a survey. BIT, 25, 2-23.
    • (1985) BIT , vol.25 , pp. 2-23
    • Arnborg, S.A.1
  • 10
    • 0002205556 scopus 로고    scopus 로고
    • Rao-Blackwellisation of sampling schemes
    • Casella, G., &: Robert, C. P. (1996). Rao-Blackwellisation of sampling schemes. Biometrika, 83(1), 81-94.
    • (1996) Biometrika , vol.83 , Issue.1 , pp. 81-94
    • Casella, G.1    Robert, C.P.2
  • 11
    • 0001249662 scopus 로고    scopus 로고
    • AIS-BN: An adaptive importance sampling algorithm for evidenctial reasoning in large baysian networks
    • Cheng, J., & Druzdzel, M. J. (2000). AIS-BN: An adaptive importance sampling algorithm for evidenctial reasoning in large baysian networks. Journal of Aritificial Intelligence Research, 13, 155-188.
    • (2000) Journal of Aritificial Intelligence Research , vol.13 , pp. 155-188
    • Cheng, J.1    Druzdzel, M.J.2
  • 12
    • 0025401005 scopus 로고
    • The computational complexity of probabilistic inferences
    • Cooper, G. (1990). The computational complexity of probabilistic inferences. Artificial Intelligence, 42, 393-405.
    • (1990) Artificial Intelligence , vol.42 , pp. 393-405
    • Cooper, G.1
  • 13
    • 0027560587 scopus 로고
    • Approximating probabilistic inference in Bayesian belief networks is NP-hard
    • Dagum, P., & Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, 60(1), 141-153.
    • (1993) Artificial Intelligence , vol.60 , Issue.1 , pp. 141-153
    • Dagum, P.1    Luby, M.2
  • 14
    • 0033188982 scopus 로고    scopus 로고
    • Bucket elimination: A unifying framework for reasoning
    • Dechter, R. (1999a). Bucket elimination: A unifying framework for reasoning. Artificial Intelligence, 113, 41-85.
    • (1999) Artificial Intelligence , vol.113 , pp. 41-85
    • Dechter, R.1
  • 15
    • 0033188982 scopus 로고    scopus 로고
    • Bucket elimination: A unifying framework for reasoning
    • Dechter, R. (1999b). Bucket elimination: A unifying framework for reasoning. Artificial Intelligence, 113(1-2), 41-85.
    • (1999) Artificial Intelligence , vol.113 , Issue.1-2 , pp. 41-85
    • Dechter, R.1
  • 17
    • 0035369663 scopus 로고    scopus 로고
    • Iterative algorithms for state estimation of jump Markov linear systems
    • Doucet, A., & Andrieu, C. (2001). Iterative algorithms for state estimation of jump Markov linear systems. IEEE Trans. on Signal Processing, 49(6), 1216-1227.
    • (2001) IEEE Trans. on Signal Processing , vol.49 , Issue.6 , pp. 1216-1227
    • Doucet, A.1    Andrieu, C.2
  • 18
    • 0001460136 scopus 로고    scopus 로고
    • On sequential Monte Carlo sampling methods for Bayesian filtering
    • Doucet, A., Andrieu, C., & Godsill, S. (2000a). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197-208.
    • (2000) Statistics and Computing , vol.10 , Issue.3 , pp. 197-208
    • Doucet, A.1    Andrieu, C.2    Godsill, S.3
  • 21
    • 4244031496 scopus 로고    scopus 로고
    • Particle filters for state estimation of jump markov linear systems
    • Tech. rep, Cambridge University Engineering Department
    • Doucet, A., Gordon, N., & Krishnamurthy, V. (1999). Particle filters for state estimation of jump markov linear systems. Tech. rep., Cambridge University Engineering Department.
    • (1999)
    • Doucet, A.1    Gordon, N.2    Krishnamurthy, V.3
  • 28
    • 84972511893 scopus 로고
    • Practical Markov Chain Monte Carlo
    • Geyer, C. J. (1992). Practical Markov Chain Monte Carlo. Statistical Science, 7, 473-483.
    • (1992) Statistical Science , vol.7 , pp. 473-483
    • Geyer, C.J.1
  • 33
    • 0000154395 scopus 로고    scopus 로고
    • Honest exploration of intractable probability distributions via Markov Chain Monte Carlo
    • Jones, G., &: Hobert, J. P. (2001). Honest exploration of intractable probability distributions via Markov Chain Monte Carlo. Statist. Sci., 16(4), 312-334.
    • (2001) Statist. Sci , vol.16 , Issue.4 , pp. 312-334
    • Jones, G.1    Hobert, J.P.2
  • 34
    • 21444441210 scopus 로고    scopus 로고
    • Unifying cluster-tree decompositions for reasoning in graphical models
    • Kask, K., Dechter, R., Larrosa, J., &: Dechter, A. (2005). Unifying cluster-tree decompositions for reasoning in graphical models. Artificial Intelligence, 166, 165-193.
    • (2005) Artificial Intelligence , vol.166 , pp. 165-193
    • Kask, K.1    Dechter, R.2    Larrosa, J.3    Dechter, A.4
  • 37
    • 84950943564 scopus 로고
    • Sequential imputations and Bayesian missing data problems
    • Kong, A., Liu, J. S., & Wong, W. (1994). Sequential imputations and Bayesian missing data problems. J. of the American Statistical Association, 85(425), 278-288.
    • (1994) J. of the American Statistical Association , vol.85 , Issue.425 , pp. 278-288
    • Kong, A.1    Liu, J.S.2    Wong, W.3
  • 38
    • 0031996401 scopus 로고    scopus 로고
    • Iterative decoding of compound codes by probability propagation in graphical models
    • Kschischang, F. R., & Frey, B. J. (1998). Iterative decoding of compound codes by probability propagation in graphical models. IEEE Journal on Selected Areas in Communications, 16, 219-230.
    • (1998) IEEE Journal on Selected Areas in Communications , vol.16 , pp. 219-230
    • Kschischang, F.R.1    Frey, B.J.2
  • 39
    • 0041781747 scopus 로고    scopus 로고
    • Boosting search with variable elimination in constraint optimization and constraint satisfaction problems
    • Larrosa, J., & Dechter, R. (2003). Boosting search with variable elimination in constraint optimization and constraint satisfaction problems. Constraints, 5(3), 303-326.
    • (2003) Constraints , vol.5 , Issue.3 , pp. 303-326
    • Larrosa, J.1    Dechter, R.2
  • 40
    • 0001006209 scopus 로고
    • Local computation with probabilities on graphical structures and their application to expert systems
    • Lauritzen, S., & Spiegelhalter, D. (1988). Local computation with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B, 50(2), 157-224.
    • (1988) Journal of the Royal Statistical Society, Series B , vol.50 , Issue.2 , pp. 157-224
    • Lauritzen, S.1    Spiegelhalter, D.2
  • 42
    • 79951870792 scopus 로고
    • The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem
    • Liu, J. (1994). The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem. Journal of the American Statistical Association, 55(427), 958-966.
    • (1994) Journal of the American Statistical Association , vol.55 , Issue.427 , pp. 958-966
    • Liu, J.1
  • 43
    • 0001789822 scopus 로고
    • Covariance structure of the Gibbs sampler with applications to the comparison of estimators and augmentation schemes
    • Liu, J., Wong, W., & Kong, A. (1994). Covariance structure of the Gibbs sampler with applications to the comparison of estimators and augmentation schemes. Biometrika, 81(1), 27-40.
    • (1994) Biometrika , vol.81 , Issue.1 , pp. 27-40
    • Liu, J.1    Wong, W.2    Kong, A.3
  • 44
    • 0030487956 scopus 로고    scopus 로고
    • Nonparametric hierarchical bayes via sequential imputations
    • Liu, J. S. (1996). Nonparametric hierarchical bayes via sequential imputations. Annals of Statistics, 24(3), 911-930.
    • (1996) Annals of Statistics , vol.24 , Issue.3 , pp. 911-930
    • Liu, J.S.1
  • 46
    • 0033466420 scopus 로고    scopus 로고
    • Sequential importance sampling for nonparametric bayes models: The next generation
    • MacEachern, S., Clyde, M., & Liu, J. (1998). Sequential importance sampling for nonparametric bayes models: The next generation. The Canadian Journal of Statistics, 27, 251-267.
    • (1998) The Canadian Journal of Statistics , vol.27 , pp. 251-267
    • MacEachern, S.1    Clyde, M.2    Liu, J.3
  • 47
    • 84972808999 scopus 로고
    • Estimating normal means with a conjugate style dirichlet process prior
    • MacEachern, S. N. (1994). Estimating normal means with a conjugate style dirichlet process prior. Communications in Statistics-Simulation and Computation, 23(3), 727-741.
    • (1994) Communications in Statistics-Simulation and Computation , vol.23 , Issue.3 , pp. 727-741
    • MacEachern, S.N.1
  • 51
    • 0023000382 scopus 로고
    • Quick medical reference (QMR) for diagnostic assistance
    • Miller, R., Masarie, F., & Myers, J. (1986). Quick medical reference (QMR) for diagnostic assistance. Medical Computing, 5(5), 34-38.
    • (1986) Medical Computing , vol.5 , Issue.5 , pp. 34-38
    • Miller, R.1    Masarie, F.2    Myers, J.3
  • 52
    • 0020001973 scopus 로고
    • Internist-1: An experimental computerbased diagnostic consultant for general internal medicine
    • Miller, R., Pople, H., & Myers, J. (1982). Internist-1: An experimental computerbased diagnostic consultant for general internal medicine. New English Journal of Medicine, 307(8), 468-476.
    • (1982) New English Journal of Medicine , vol.307 , Issue.8 , pp. 468-476
    • Miller, R.1    Pople, H.2    Myers, J.3
  • 54
    • 0023590155 scopus 로고
    • Using causal knowledge to create simulated patient cases: The CPCS project as an extension of INTERNIST-1
    • Parker, R., & Miller, R. (1987). Using causal knowledge to create simulated patient cases: the CPCS project as an extension of INTERNIST-1. In Proceedings of the 11th Symp. Comp. Appl. in Medical Care, pp. 473-480.
    • (1987) Proceedings of the 11th Symp. Comp. Appl. in Medical Care , pp. 473-480
    • Parker, R.1    Miller, R.2
  • 56
    • 0026140542 scopus 로고
    • Fusion and propagation with multiple observations in belief networks
    • Peot, M. A., & Shachter, R. D. (1992). Fusion and propagation with multiple observations in belief networks. Artificial Intelligence, 48, 299-318.
    • (1992) Artificial Intelligence , vol.48 , pp. 299-318
    • Peot, M.A.1    Shachter, R.D.2
  • 59
    • 0000051645 scopus 로고    scopus 로고
    • Updating schemes; correlation structure; blocking and parameterization for the Gibbs sampler
    • Roberts, G. O., & Sahu, S. K. (1997). Updating schemes; correlation structure; blocking and parameterization for the Gibbs sampler. Journal of the Royal Statistical Society, Series B, 59(2), 291-317.
    • (1997) Journal of the Royal Statistical Society, Series B , vol.59 , Issue.2 , pp. 291-317
    • Roberts, G.O.1    Sahu, S.K.2
  • 60
    • 0001514831 scopus 로고    scopus 로고
    • Bounds on regeneration times and convergence rates for Markov chains
    • Roberts, G. O., &: Tweedie, R. L. (1999). Bounds on regeneration times and convergence rates for Markov chains. Stochastic Processes and Their Applications, 80, 211-229.
    • (1999) Stochastic Processes and Their Applications , vol.80 , pp. 211-229
    • Roberts, G.O.1    Tweedie, R.L.2
  • 61
    • 0011733206 scopus 로고    scopus 로고
    • Corregendum to bounds on regeneration times and convergence rates for Markov chains
    • Roberts, G. O., & Tweedie, R. L. (2001). Corregendum to bounds on regeneration times and convergence rates for Markov chains. Stochastic Processes and Their Applications, 91, 337-338.
    • (2001) Stochastic Processes and Their Applications , vol.91 , pp. 337-338
    • Roberts, G.O.1    Tweedie, R.L.2
  • 62
    • 0000926886 scopus 로고
    • Convergence rates for Markov Chains
    • Rosenthal, J. S. (1995). Convergence rates for Markov Chains. SIAM Review, 57(3), 387-405.
    • (1995) SIAM Review , vol.57 , Issue.3 , pp. 387-405
    • Rosenthal, J.S.1
  • 63
    • 4544302569 scopus 로고    scopus 로고
    • Rosti, A.-V., & Gales, M. (2004). Rao-Blaekwellised Gibbs sampling for switching linear dynamical systems. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), PP- 809-812.
    • Rosti, A.-V., & Gales, M. (2004). Rao-Blaekwellised Gibbs sampling for switching linear dynamical systems. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), PP- 809-812.
  • 67
    • 0001673255 scopus 로고    scopus 로고
    • Convergence properties of the batch means method for simulation output analysis
    • Steiger, N. M., & Wilson, J. R. (2001). Convergence properties of the batch means method for simulation output analysis. INFORMS Journal on Computing, 13(4), 277-293.
    • (2001) INFORMS Journal on Computing , vol.13 , Issue.4 , pp. 277-293
    • Steiger, N.M.1    Wilson, J.R.2
  • 68
    • 0000576595 scopus 로고
    • Markov chains for exploring posterior distributions
    • Tierney, L. (1994). Markov chains for exploring posterior distributions. Annals of Statistics, A2(4), 1701-1728.
    • (1994) Annals of Statistics , vol.A2 , Issue.4 , pp. 1701-1728
    • Tierney, L.1


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