Multi-domain sampling with applications to structural inference of bayesian networks

Journal of the American Statistical Association

Volumn 106, Issue 496, 2011, Pages 1317-1330

  Zhou, Qing ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Domain based representation; Monte carlo; Multimodal distribution; Network structure; Protein signaling network; Wang landau algorithm

Indexed keywords

EID: 84855984607     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/jasa.2011.ap10346     Document Type: Article

References (41)

1. Andrieu, C., and Moulines, E. (2006), "On the Ergodicity Properties of Some Adaptive MCMC Algorithms," Annals of Applied Probability, 16, 1462-1505. [1320]
2. Andrieu, C., Moulines, E., and Priouret, P. (2005), "Stability of Stochastic Approximation Under Verifiable Conditions," SIAM Journal of Control and Optimization, 44, 283-312. [1320,1329]
3. Atchadé, Y. F., and Liu, J. S. (2010), "TheWang-Landau Algorithm in General State Spaces: Applications and Convergence Analysis," Statistica Sinica, 20, 209-233. [1317,1320,1321,1329]
4. Becker, O. M., and Karplus, M. (1997), "The Topology of Multidimensional Potential Energy Surface: Theory and Application to Peptide Structure and Kinetics," Journal of Chemical Physics, 106, 1495-1517. [1327]
5. Chen, M. H., Shao, Q., and Ibrahim, J. G. (2001), Monte Carlo Methods in Bayesian Computation. New York: Springer. [1317]
6. Cooper, G. F., and Herskovits, E. (1992), "A Bayesian Method for the Induction of Probabilistic Networks From Data," Machine Learning, 9, 309-347. [1323]
7. Cooper, G. F., and Yoo, C. (1999), "Causal Discovery From a Mixture of Experimental and Observational Data," in Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, San Francisco, CA: Morgan Kaufmann, pp. 116-125. [1323]
8. Ellis, B., and Wong, W. H. (2008), "Learning Causal Bayesian Network Structures From Experimental Data," Journal of the American Statistical Association, 103, 778-789. [1318,1323,1326]
9. Friedman, N., and Koller, D. (2003), "Being Bayesian About Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks," Machine Learning, 50, 95-126. [1318]
10. Gelfand, A. E., and Smith, A. F. M. (1990), "Sampling-Based Approaches to Calculating Marginal Densities," Journal of the American Statistical Association, 85, 398-409. [1317]
11. 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.[1317]
12. Geyer, C. J. (1991), "Markov Chain Monte Carlo Maximum Likelihood," in Computing Science and Statistics: Proc. 23rd Symp. Interface, ed. E. M. Keramidas, Fairfax Station, VA, pp. 156-163. [1319]
13. Gilks, W. R., Roberts, R. O., and George, E. I. (1994), "Adaptive Direction Sampling," The Statistician, 43, 179-189.[1327]
14. Gordon, V. S., and Whitley, D. (1993), "Serial and Parallel Genetic Algorithms as Function Optimizers," in Proc. 5th Int. Conf. Genetic Algorithms, San Mateo, CA, pp. 177-183. [1322]
15. Haario, H., Saksman, E., and Tamminen, J. (2001), "An Adaptive Metropolis Algorithm," Bernoulli, 7, 223-242. [1319]
16. Hastings, W. K. (1970), "Monte Carlo Sampling Methods Using Markov Chains and Their Applications," Biometrika, 57, 97-109.[1317]
17. Hindley, A., and Kolch, W. (2007), "Raf-1 and B-Raf Promote Protein Kinase C θ Interaction With BAD," Cellular Signalling, 19, 547-555.[1326]
18. Holland, P. W. (1988), "Causal Inference, Path Analysis, and Recursive Structural Equations Models," in Sociological Methodology, ed. C. C. Clogg, Washington, DC: American Sociological Association, pp. 449-484. [1323]
19. Li, Z., and Scheraga, H. A. (1987), "Monte Carlo-Minimization Approach to the Multiple-Minima Problem in Protein Folding," Proceedings of the National Academy of Sciences, USA, 84, 6611-6615.[1327]
20. Liang, F. (2005), "GeneralizedWang-Landau Algorithm for Monte Carlo Computation," Journal of the American Statistical Association, 100, 1311-1327. [1317]
21. Liang, F. (2009), "On the Use of Stochastic Approximation Monte Carlo for Monte Carlo Integration," Statistics and Probability Letters, 79, 581-587. [1321]
22. Liang, F., and Zhang, J. (2009), "Learning Bayesian Networks for Discrete Data," Computational Statistics and Data Analysis, 53, 865-876.[1318]
23. Liang, F., Liu, C., and Carroll, J. (2007), "Stochastic Approximation in Monte Carlo Computation," Journal of the American Statistical Association, 102, 305-320. [1317,1320,1329]
24. Liu, J. S. (2008), Monte Carlo Strategies in Scientific Computing (2nd ed.), New York: Springer. [1317]
25. Liu, J. S., Liang, F., and Wong, W. H. (2000), "The Multiple-Try Method and Local Optimization in Metropolis Sampling," Journal of the American Statistical Association, 95, 121-134.[1327]
26. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953), "Equations of State Calculation by Fast Computing Machines," Journal of Chemical Physics, 21, 1087-1091.[1317]
27. Neyman, J. (1990), "Sur les applications de la thar des probabilities aux experience Agaricales: Essay des principle" [English translation of excerpts by D. Dabrowska and T. Speed], Statistical Science, 5, 463-472. [1323]
28. Pearl, J. (1993), "Graphical Models: Causality and Intervention," Statistical Science, 8, 266-273. [1323]
29. Pearl, J. (2000), "The Logic of Counterfactuals in Causal Inference," Journal of the American Statistical Association, 95, 428-435. [1323]
30. Roberts, G. O., and Tweedie, R. L. (1996), "Geometric Convergence and Central Limit Theorems for Multidimensional Hastings and Metropolis Algorithms," Biometrika, 83, 95-110. [1329]
31. Robins, J. (1986), "A New Approach to Causal Inference in Mortality Studies With Sustained Exposure Periods: Application to Control of the Healthy Worker Survivor Effect," Math Modeling, 7, 1393-1512. [1323]
32. Robins, J (1987), "A Graphical Approach to the Identification and Estimation of Causal Parameters in Mortality Studies With Sustained Exposure Periods," Journal of Chronic Diseases, 40, 1395-1615. [1323]
33. Rubin, D. (1978), "Bayesian Inference for Causal Effects: The Role of Randomization," The Annals of Statistics, 6, 34-58. [1323]
34. Sachs, K., Perez, O., Peer, D., Lauffenburger, D. A., and Nolan, G. P. (2005), "Causal Protein-Signalling Networks Derived From Multiparameter Single- Cell Data," Science, 308, 523-529. [1325]
35. Spirtes, P., Glymour, C., and Scheines, R. (1993), Causation, Prediction, and Search (2nd ed.), Cambridge, MA: MIT Press.[1323]
36. Tanner, M. A., and Wong, W. H. (1987), "The Calculation of Posterior Distributions by Data Augmentation," Journal of the American Statistical Association, 82, 528-550.[1317]
37. Wang, F., and Landau, D. P. (2001), "Efficient Multiple-Range Random-Walk Algorithm to Calculate the Density of States," Physical Review Letters, 86, 2050-2053. [1317,1320]
38. Wales, D. J., and Bogdan, T. V. (2006), "Potential Energy and Free Energy Landscapes," Journal of Physical Chemistry B, 110, 20765-20776.[1327]
39. Wales, D. J., and Doye, J. P. K. (1997), "Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms," Journal of Physical Chemistry A, 101, 5111-5116. [1327]
40. Zhou, Q. (2011), "Random Walk Over Basins of Attraction to Construct Ising Energy Landscapes," Physical Review Letters, 106, 180602. [1328]
41. Zhou, Q., and Wong, W. H. (2008), "Reconstructing the Energy Landscape of a Distribution From Monte Carlo Samples," Annals of Applied Statistics, 2, 1307-1331. [1327]