References: |
[1] P. Anderson and J.Q. Smith (2005). A graphical framework for represent- ing the semantics of asymmetric models. Technical report 05-12, CRiSM, Department of Statistics, The University of Warwick. [2] T. Bedford and R. Cooke (2001). Probabilistic risk analysis: foundations and methods. Cambridge University Press, Cambridge. [3] C. Boutilier, N. Freidman, M. Goldszmidt and D. Koller (1996). Context- specific independence in Bayesian networks. In Proceedings of UAI - 96 115-123. [4] R.E. Bryant (1986). Graphical algorithms for Boolean function manipu- lation. IEEE Transactions of Computers C-35 677-691. [5] R.J. Castelo (2002). The Discrete Acyclic Digraph Markov Model in Data Mining. Utrecht University, Ph.D. thesis. [6] R. Castelo and M.D. Perlman (2004). Learning essential graph Markov models from data. In A. Gamze, S. Moral and A. Salmeron (eds.) Ad- vances in Bayesian networks, Springer, Berlin, 255–269. [7] G.A. Churchill (1995). Accurate restoration of DNA sequences. In C. Gatsaris et al. (eds.) Case Studies in Bayesian Statistics vol. II, Springer- Verlag, New-York, 90-148. [8] D. Cooper and C. Yoo (1999). Causal discovery from a mixture of ex- perimental and observational data. In K.B. Laskey and H. Prade (eds.) Proceedings of the Seventeen Conference on Uncertainty in Artificial In- telligence, Morgan Kaufmann, San Francisco. [9] R.G. Cowell, A.P. Dawid, S.L. Lauritzen and D.J. Spiegelhalter (1999). Probabilistic Networks and Expert Systems, Springer-Verlag, New York. [10] A.P. Dawid (2000). Causality without counterfactuals J. Amer. Statist. Ass. 95:407-448. [11] D.G.T. Denison, C.C. Holmes, B.K. Mallick and A.F.M. Smith (2002). Bayesian methods for nonlinear classification and regression, John Wiley & Sons Ltd., Chichester. [12] N. Freidman and M. Goldszmidt (1999). Learning Bayesian networks with local structure. In M.I. Jordan (ed.) Learning in Graphical Models MIT Press, 421-459. [13] S. French (ed.) (1989). Readings in Decision analysis. Chapman and Hall/CRC, London. [14] D. Glymour and G.F. Cooper (1999). Computation, Causation, and Dis- covery. MIT Press, Cambridge, MA. [15] D. Hausman (1998). Causal Asymmetries, Cambridge University Press, Cambridge. [16] P.W. Holland (1986). Statistics and causal inference (With discussion and a reply by the author). Journal of the American Statistical Association, 81(396):945–970. [17] M.I. Jordan (ed.) (1999). Learning in Graphical Models, MIT Press, Cam- bridge, MA. [18] M. Jaeger (2004). Probabilistic decision graphs - combining verification and AI techniques for probabilistic inference. Int.J. of Uncertainty, Fuzzi- ness and Knowledge-based Systems, 12:19-42. [19] S.L. Lauritzen (1996). Graphical models. Oxford Science Press, Oxford, 1st edition. [20] R. Lyons (1990). Random walks and percolation on trees. Annals of Prob- ability, 18:931-958. [21] D. Madigan and A. E. Raftery (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window. Journal of the American Statistical Association 89(428): 1535-1546. [22] A.M. Madrigal and J.Q. Smith (2004). Causal Identification in Design Networks. In Sucar et al. (eds.) Advances in Artificial Intelligence 2, Springer-Verlag, 517-526. [23] A.M. Madrigal (2004). Evaluations of Policy Interventions under Exper- imental Conditions using Bayesian Influence Diagrams. The University of Warwick, Ph.D. Thesis. [24] D. McAllester, M. Collins and F. Pereira (2004). Case-factor diagrams for structured probability models. In Proceedings of UAI - 2004 382-391. [25] J. Pearl (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kauffman, San Mateo. [26] J. Pearl (1995). Causal diagrams for empirical research. Biometrika, 82:669-710. [27] J. Pearl (2000). Causality. models, reasoning and inference. Cambridge University Press, Cambridge. [28] J. Pearl (2003). Statistics and Causal Inference: A Review (with discus- sion). Test, 12(2):281-345. [29] D. Poole and N.L. Zhang (2003). Exploiting contextual independence in probabilistic inference. Journal of Artificial Intelligence research, 18:263- 313. [30] E. Riccomagno and J.Q. Smith (2003). Non-Graphical Causality: a gen- eralisation of the concept of a total cause. Research report series No. 394, Dept of Statistics, The University of Warwick. [31] E. Riccomagno and J.Q. Smith (2004). Identifying a cause in models which are not simple Bayesian networks. In Proceedings of the 10th Con- ference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 1315 -1322. [32] J.M. Robins (1986). A new approach to causal inference in mortality studies with a sustained exposure period —application to control of the healthy worker survivor effect. Mathematical Modeling, 7:1393-1512. [33] J.M. Robins (1997). Causal inference from complex longitudinal data. In M. Berkane (ed.) Latent variable modeling and applications to causality (Los Angeles, CA, 1994). Springer-Verlag, New York, 69–117. [34] J.M. Robins, R. Scheines, P. Spirtes, and L. Wasserman (2003). Uniform Consistency in Causal Inference. Biometrika 90(3):491-515. [35] D. Rubin (1973). Estimating causal effects of treatments in randomised and non - randomised studies. J. Educational Psychology 66:688-701. [36] G. Shafer (1996). The Art of Causal Conjecture. MIT Press, Cambridge, MA. [37] R. Settimi and J.Q. Smith (1998). On the geometry of Bayesian graph- ical models with hidden variables. In G. Cooper and S. Moral (eds.) Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, S. Francisco, 472–479. [38] R. Settimi and J.Q. Smith (2000). Geometry, moments and conditional independence trees with hidden variables. The Annals of Statistics, 28(4):1179-1205. [39] J.Q. Smith, A.E. Faria, S. French, D. Ranyard, J. Bohunova, T. Dura- nova, M. Stubna, L. Dutton, C. Rojas and A. Sohier (1997). Probabilistic data assimilation within RODOS. Radiation Protection Dosimetry 73(1- 4):57-59. [40] J.Q. Smith and E. E. Anderson (2006). Conditional independence and Chain Event Graphs. Artificial Intelligence, to appear. [41] J.Q. Smith and J. Croft (2003). Bayesian networks for discrete multivari- ate data: An algebraic approach to inference. J. of Multivariate Analysis, 84(2):387-402. [42] D. Spiegelhalter, A.P. Dawid, S.L. Lauritzen and R.G. Cowell (1993). Bayesian analysis of expert systems. Statistical Science, 8:219-282. [43] P. Spirtes, C. Glymour and R. Scheines (1993). Causation, Prediction, and Search. Springer-Verlag, New York. |