Bayesian decision analysis: Principles and practice

Bayesian Decision Analysis: Principles and Practice

Volumn , Issue , 2010, Pages 1-338

  Smith, Jim Q ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

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[No Author keywords available]

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EID: 84924507537     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511779237     Document Type: Book

References (255)

1. Aitken, C. and Taroni, F. (2004) “Statistics and the Evaluation of Evidence for Forensic Scientists”. 2nd edn. Wiley, Chichester.
2. Aitken, C. G. G., Taroni, F. and Garbolino, P. (2003) “A graphical model for the evaluation of cross-transfer evidence in DNA profiles”. Theoretical Population Biology 63, 179–190.
3. Andersson, S., Madigan, D. and Pearlmean, M. (1997) “Alternative Markov properties for chain graphs”. Scandinavian Journal of Statistics 24, 81–102.
4. Andrade, J. A. A. and O’Hagan, A. (2006) “Bayesian robustness modelling using regularly varying distributions”. Bayesian Analysis 1, 169–188.
5. Atwell, D. N. and Smith, J. Q. (1991) “ABayesian forecasting model for sequential bidding”. Journal of Forecasting 10, 565–577.
6. Bedford, T. and Cooke, R. (2001) “Probabilistic Risk Analysis: Foundation and Methods”. Cambridge University Press.
7. Bensoussen, A. (1992) “Stochastic Control of Partially Observable Systems”. Cambridge University Press.
8. Berger, J. O. (1985) “Statistical Decision Theory and BayesianAnalysis”. 2nd edn. Springer-Verlag, New York.
9. Bernardo, J. M. and Smith, A. F. M. (1996) “Bayesian Theory”. Wiley, Chichester.
10. Bersekas, D. P. (1987) “Dynamic Programming”. Prentice Hall, Englewood Cliffs.
11. Blackwell, D. and Dubins, L. (1962) “Merging of opinions with increasing information”. Annals of Mathematical Statistics 33, 882–886.
12. Bonet, B. (2001a) “A calculus for causal relevance”. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (eds. J. Breese, D. Koller). Morgan Kaufmann, San Francisco, pp. 40–47.
13. Bonet, B. (2001b) “Instrumentality tests revisited”. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (eds. J. Breese, D. Koller). Morgan Kaufmann, San Francisco, pp. 48–54.
14. Caines, P., Deardon, R. and Wynn, H. (2002) “Conditional orthogonality and conditional stochastic realization”. In: New Directions in Mathematical Systems Theory and Optimization, LNCIS 286 (eds. A. Rantzer and C. I. Byrnes). Springer-Verlag, New York, pp. 71–84.
15. Canning, C., Thompson, E.A. and Skolnick, M. H. (1978) “Probability functions on complex pedigrees”. Advances in Applied Probability 10, 26–61.
16. Carlson, B. W. (1993) “The accuracy of future forecasts and past judgements”. Organizational Behaviour and Human Decision Processes 54, 245–276.
17. Chen, M.-H., Shao, Q.-M. and Ibrahim, J. G. (2000) “Monte Carlo Methods in Bayesian Computation”. Springer-Verlag, New York.
18. Clemen, R. T. and Lichtendahl, K. C. (2002) “Debiasing expert overconfidence: a Bayesian calibration model”. Working paper, Duke University, Durham NC.
19. Clemen, R. T. and Winkler, R. L (2007) “Aggregating probability distributions”. In: Advances in Decision Analysis: From Foundations to Applications (eds. W. Edwards, R. F. Miles, Jr. and D. von Winterfeldt). Cambridge University Press, pp. 154–176.
20. Covaliu, Z. and Oliver R. M. (1995) “Representation and solution of decision problems using sequential decision diagrams”. Management Science 41, 1860–1881.
21. Cowell, R. G., Dawid, A. P., Lauritzen, S. L. and Spiegelhalter, D. J. (1999) “Probabilistic Networks and Expert Systems”. Springer-Verlag, New York.
22. Cox, D. R. and Wermuth, N. (1996) “Multivariate Dependences”. Chapman and Hall, London.
23. Curley, S. P. (2008) “Subjective probability”. In: Encyclopedia of Quantitative Risk Analysis and Assessment (eds. E. L. Melnick and B. S. Everitt). Wiley, Chichester, pp. 1725–1734.
24. Dahlhaus, R. and Eichler, M. (2003) “Causality and graphical models for time series”. In: Highly Structured Stochastic Systems (eds. P. Green, N. Hjort and S. Richardson). Oxford University Press, pp. 115–137.
25. Daneshkhah, A. and Smith, J. Q. (2004) “Multicausal prior families, randomisation and essential graphs”. In: Advances in Bayesian Networks, Physica-Verlag, 1–17 and Proceedings of the First European Workshop on Probabilistic Models Cuenca Spain, 25–34.
26. Dawid, A. P. (1973) “Posterior expectations for large observations”. Biometrika 60, 664–667.
27. Dawid, A. P. (1979) “Conditional independence in statistical theory (with discussion)”. Journal of the Royal Statistical Society B 41(1), 1–31.
28. Dawid, A. P. (1982) “The well calibrated Bayesian (with discussion)”. Journal of the American Statistical Association 77, 604–613.
29. Dawid, A. P. (1992) “Prequential analysis, stochastic complexity and Bayesian inference”. In: Bayesian Statistics (eds. J. M. Bernardo et al.). Oxford University Press, pp. 109–125.
30. Dawid, A. P. (2000) “Causality without counterfactuals (with discussion)”. Journal of the American Statistical Association 95, 407–448.
31. Dawid, A. P. (2001) “Separoids: a mathematical framework for conditional independence and irrelevance”. Annals of Mathematics and Artificial Intelligence 32, 335–372.
32. Dawid, A. P. (2002a) “Influence diagrams for causal modelling and inference”. International Statistical Reviews 70, 161–189.
33. Dawid, A. P. (2002b) “Bayes theorem and the weighing of evidence by juries”. In: Proceedings of the British Academy, vol. 113 (ed. R. Swinburne). Oxford University Press, pp. 71–90.
34. Dawid, A. P. (2007) “The geometry of proper scoring rules”. Annals of the Institute of Statistical Mathematics 59(1), 77–93.
35. Dawid, A. P. and Evett, I. W. (1997) “Using a graphical model to assist the evaluation of complicated patterns of evidence”. Journal of Forensic Science 42, 226–231.
36. Dawid, A. P. and Lauritzen, S. (1993) “Hyper-Markov laws in the statistical analysis of decomposable graphical models”. Annals of Statistics 21(3), 1272–1317.
37. Dawid, A. P. and Vovk, V. G. (1999) “Prequential probability: principles and properties”. Bernoulli 5, 125–162.
38. DeFinetti, B. (1974) “Theory of Probability, Vol. 1”. Wiley, Chichester.
39. De Finetti, B. (1980) “Foresight, its logical laws, its subjective sources”. In: Studies in Subjective Probability (eds. H. E. Kyburg and H. E. Smokler). Dover Publications, New York, pp. 93–158.
40. De Groot, M. H. (1970) “Optimal Statistical Decisions”. McGraw-Hill, New York.
41. Dean, T. and Kanazawa, K. (1988) “Probabilistic temporal reasoning”. Proceedings AAAI-88, 524–528.
42. Denison, D. G. T., Holmes, C. C., Mallick, B. K. and Smith, A. F. M. (2005) “Bayesian Methods for Non-linear Classification and Regression”. Wiley, Chichester.
43. DeRobertis, L. (1978) “The use of partial prior knowledge in Bayesian inference”. Ph. D. dissertation, Yale University.
44. Didelez, V. (2008) “Graphical models for marked point processes based on local independence”. Journal of the Royal Statistical Society, Series B 70, 245–264.
45. Dodd, L., Moffat, J. and Smith, J. Q. (2006) “Discontinuity in decision making when objectives conflict: a military command decision case study”. Journal of the Operational Research Society 57, 643–654.
46. Dowie, J. (1976) “On the efficiency and equity of betting markets”. Economica 43,139–150.
47. Drton, M. and Richardson, T. S. (2008) “Binary models for marginal independence”. Journal of the Royal Statistical Society B 70(2), 287–310.
48. Durbin, J. and Koopman, S. J. (2001) “Time Series Analysis by State Space Methods”. Oxford University Press.
49. Edwards, D. (2000) “Introduction to Graphical Modelling”. Springer-Verlag, New York.
50. Eichler, M. (2006) “Graphical modelling of dynamic relationships in multivariate time series”. In: Handbook of Time Series Analysis (eds. M. Winterhalder, B. Schelter and J. Timmer). Wiley-VCH, Berlin, pp. 335–372.
51. Eichler, M. (2007) “Granger-causality and path diagrams for multivariate time series”. Journal of Econometrics 137, 334–353.
52. Evans, M. and Swartz, T. (2000) “Approximating Integrals via Monte Carlo and Deterministic Methods”. Oxford University Press.
53. Faria, A. E. and Smith, J. Q. (1996) “Conditional External Bayesianity in Decomposable Influence Diagrams”. In: Bayesian Statistics 5 (eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and J. Q. Smith). Oxford University Press, pp. 551–560.
54. Faria, A. E. and Smith, J. Q. (1997) “Conditionally externally Bayesian pooling operators in chain graphs”. Annals of Statistics 25(4), 1740–1761.
55. Feller, W. (1971) “An Introduction to Probability Theory and its Applications, Vol. 2”. 2nd edn. John Wiley, New York.
56. Fine, T. L. (1973) “Theories of Probability: an Examination of Foundations 2”. Academic Press, New York.
57. Flores, M. J. and Gamez, J. A. (2006) “A review on distinct methods and approaches to perform triangulation for Bayesian networks”. In: Advances in Probabilistic Graphical Models (eds. J. A. Gamez and A. Salmeron). Springer-Verlag, New York, pp. 127–152.
58. Freeman, G. and Smith, J. Q. (2010) “Bayesian MAP Selection of Chain Event graphs”. Journal of Multivariate Analysis (to appear).
59. Freeman, G. H., Jacka, S. D., Shaw, J. E. H. and Smith, J. Q. (1996) “Modelling the management of underground water assets”. Journal of Applied Statistics 23(2,3), 273–284.
60. French, S. and Rios Insua, D. (2000) “Statistical Decision Theory”. Kendall’s Library of Statistics 9. Arnold, London.
61. French, S., Papamichail, K. N., Ranyard, D. C. and Smith, J. Q. (1995) “Decision support for nuclear emergency response”. Proceedings of the Fifth Hellenistic Conference on Informatics, Athens, Vol. 2, pp. 591–600.
62. French, S., Harrison, M. T. and Ranyard, D. C. (1997) “Event conditional attributer modelling in decision making when there is a threat of a nuclear accident”. In: The Practice of Bayesian Analysis (eds. S. French and J. Q. Smith). Arnold, London.
63. French, S., Papamichail, K. N., Ranyard, D. C. and Smith, J. Q. (1998) “Design of a decision support system for the use in the event of a radiation accident”. In: Applied Decision Analysis (eds. F. J. Giron andM. L. Martinez). KluwerAcademic, Dordrecht, pp. 3–18.
64. French, S., Maule, J. and Papamichail, N. (2009) “Decision Behaviour, Analysis and Support”. Cambridge University Press.
65. Fruthwirth-Schnatter, S. (2006) “Finite Mixture and Markov Switching Models”. Springer- Verlag, New York.
66. Gammerman, D. and Lopez, H. F. (2006) “Markov Chain Monte Carlo”. Chapman and Hall, London.
67. Geiger, D. and Heckerman, D. (1997) “A characterization of the Dirichlet distribution through local and global independence”. Annals of Statistics 25, 3, 731–792.
68. Geiger, D. and Pearl, J. (1990) “On the Logic of Causal Models in Uncertainty in Artificial Intelligence, Vol. 4” (eds. R. D. Shachter, T. S. Lewitt, L. N. Kanal and J. F. Lemmer). North Holland, Amsterdam, pp. 3–14.
69. Geiger, D. and Pearl, J. (1993) “Logical and algorithmic properties of conditional independence and graphical models”. Annals of Statistics 21, 2001–2021.
70. Gelman, A. and Hill, J. (2007) “Data Analysis using Regression and Multilevel/Hierarchical Models”. Cambridge University Press.
71. Gelman, A., Carlin, J. B., Stern, H. S. and Rubib, D. B. (1995) “Bayesian Data Analysis”. Chapman and Hall, London.
72. Ghosh, J. K. and Ramamoorthi, R. V. (2003) “Bayesian Nonparametrics”. Springer-Verlag, New York.
73. Gigerenzer, G. (2002) “Reckoning with Risk”. Penguin, London.
74. Glymour, D. and Cooper, G. F. (1999) “Computation, Causation, and Discovery”. MIT Press, Cambridge, MA.
75. Goldsein, M. (1985) “Temporal Coherence”. In: Bayesian Statistics 2 (eds. J. M. Bernardo et al.). Oxford University Press, pp. 189–209.
76. Goldstein, M. and Rougier, J. C. (2009) “Reified Bayesian modelling and inference for physical systems”. Journal of Statistical Planning and Inference 139(3), 1221–1239.
77. Goldstein, M. and Wooff, D. (2007) “Bayesian Linear Statistic: Theory and Methods”. Wiley, Chichester.
78. Gomez, M. (2004) “Real world applications of influence diagrams”. In: Advances in Bayesian Networks (eds. J. A. Gamez, S. Moral, A. S. Cerdan). Springer-Verlag, New York, pp. 161–180.
79. Goodwin, P. and Wright, G. (2003) “Decision Analysis for Management Judgement”. 3rd edn. John Wiley and Sons, Chichester.
80. Grimmet, G. R. and Stirzaker, D. R. (1982) “Probability and Random Processes”. Oxford University Press.
81. Gustafson, P. and Wasserman, L. (1995) “Local sensitivity diagnostics for Bayesian inference”. Annals of Statistics 23, 2153–2167.
82. Harrison, P. J. and Smith, J. Q. (1979) “Discontinuous decisions and conflict”. In: Proceedings of the First International Meeting in Bayesian Statistics, Valencia, Spain, pp. 99–127.
83. Heard, N. A., Holmes, C. C. and Stephens, D. A. (2006) “A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes: an application of Bayesian hierarchical clustering of curves”. Journal of the American Statistical Association 101,473, 18–29.
84. Heath, D. and Sudderth, W. (1989) “Coherent inference from improper priors and from finitely additive priors”. Annals of Statistics 17, 2, 907–919.
85. Heckerman D. (1998) “A tutorial to learning with Bayesian networks”. In: Learning in Graphical Models (ed. M. I. Jordon). MIT Press, Cambridge, MA, pp. 301–354.
86. Heckerman, D. (2007) “A Bayesian approach to learning causal networks”. In: Advances in Decision Analysis: From Foundations to Applications (eds. W. Edwards, R. F. Miles Jr., D. von Winterfeldt). Cambridge University Press, pp. 202–220.
87. Hill, B. M. (1988) “De Finnetti’s theorem, induction, A(n) or Bayesian nonparametric inference”. In: Bayesian Statistics 3 (eds. J. M. Bernardo et al.). Oxford University Press, pp. 211–241 (with discussion).
88. Hoffman, K. and Kunze, R. (1971) “Linear Algebra”. 2nd edn. Prentice Hall, New Jersey.
89. Hora, S. C. (2007) “Eliciting probabilities from experts”. In: Advances in Decision analysis: From Foundations to Applications (eds. W. Edwards, R. F. Miles Jr., D. von Winterfeldt). Cambridge University Press, pp. 129–153.
90. Howard, R. A. (1988) “Decision analysis: practice and promise”. Management Science 34(6), 679–695.
91. Howard, R.A. (1990) “From influence to relevance to knowledge”. In: Influence Diagrams, Belief Nets and Decision Analysis (eds. R. M. Oliver, J. Q. Smith). Wiley, Chichester, pp. 3–23.
92. Howard, R.A. and Matheson, J. E. (1984) “Readings on the Principles and Applications of Decision Analysis, Vol. 2”. Strategic Decision Group, Menlo Park, CA, pp. 719–762.
93. Ibrahim, J. G. and Chen, M. H. (2000) “Power prior distributions for regression models”. Statistical Science 15, 46–60.
94. Imbens, G. W. and Rubin, D. R. (1997) “Bayesian inference for causal effects in randomised experiments with noncompliance”. Annals of Statistics 25, 305–327.
95. Jaegar, M. (2004) “Probability decision graphs - combining verification and AI techniques for probabilistic inference”. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 12, 19–42.
96. Jensen, F. V. and Nielsen, T. D. (2007) “Bayesian Networks and Decision Graphs”. 2nd edn. Springer-Verlag, New York.
97. Jensen, F. V., Neilsen, T. D. and Shenoy, P. P. (2004) “Sequential influence diagrams: a unified asymmetry framework”. In: Proceedings of the Second European Workshop on Probabilistic Graphical Models (ed. P. Lucas). Leiden, the Netherlands, pp. 121–128.
98. Jorion, P. (1991) “Bayesian and CAPM estimators of the means: Implications for portflio selection”. Journal of Banking and Finance 15, 717–727.
99. Kadane, J. B. and Chuang, D. (1978) “Stable decision problems”. Annals of Statistics 6, 1095–1110.
100. Kadane, J. B. and Larkey, P. D. (1982) “Subjective probability and the theory of games”. Management Science 28(2), 113–120.
101. Kadane, J. and Winkler, R. L. (1988) “Separating probability elicitation from utilities”. Journal of the American Statistical Association 83, 357–363.
102. Kadane, J. R., Schervish, M. J. and Seidenfeld, T. (1986) “Statistical implications of finitely additive probability”. In: Bayesian Inference and Decision Techniques (eds. P. K. Goel and A. Zellner). Elsevier, Amsterdam, pp. 59–76.
103. Kahenman, D. and Tversky, A. (1979) “Prospect theory: an analysis of decision under risk”. Econometrika 47, 263–291.
104. Keeney, R. L. (1974) “Multiplicative utility functions”. Operations Research 22, 22–34.
105. Keeney, R. L. (1992) “Value-focussed Thinking: A Path to Creative Decision Making”. Harvard University Press.
106. Keeney, R. L. (2007) “Developing objectives and attributes”. In: Advances in Decision Analysis: From Foundations to Applications (eds W. Edwards, R. F. Miles Jr., D. von Winterfeldt). Cambridge University Press, pp. 104–128.
107. Keeney, R. L. and Raiffa, H. (1976) “Decisions with Multiple Objectives: Preferences and Value Trade-offs”. John Wiley and Sons, New York.
108. Kjaerulff, U. B. and Madsen, A. L. (2008) “Belief Networks and Influence Diagrams: A Guide to Construction and Analysis”. Springer-Verlag, New York.
109. Kleinmauntz, B., Fennema, M. G. and Peecher, M. E. (1996) “Conditional assessment of probabilities: identifying the benefits of decomposition”. Organizational Behaviour and Human Decision Processes 66, 1–15.
110. Koehler, D. J., White, C. M. and Grondin, R. (2003) “An evidential support accumulation model of subjective probability”. Cognitive Psychology 46, 152–197.
111. Koeller, D. and Friedman, N. (2009) “Probabilistic Graphical Models: Principles and Techniques”. MIT Press, Cambridge, MA.
112. Koeller, D. and Lerner, U. (1999) “Sampling in factored dynamic systems”. In: Sequential Monte Carlo Methods in Practice (eds. A. Doucet, N. de Freitas and N. Gordon). Springer-Verlag, New York, pp. 445–464.
113. Korb, K. B. and Nicholson, A. E. (2010) “Bayesian Artificial Intelligence”. Chapman and Hall, London.
114. Korchnoi, V. (2001), “My Best Games, Vol. 2”. Olms Press.
115. Koster, J. T. A. (1996) “Markov properties of non-recursive causal models”. Annals of Statistics 24, 2148–2177.
116. Kurth, T., Walker, A. M., Glynn, R. J., et al. (2006) “Results of multivariable logistic regression, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect”. American Journal of Epidemiology 162(5), 471–478.
117. Lad, F. (1996) “Operational Subjective Statistical Methods”. John Wiley and Sons, New York.
118. Lancaster, T. (2004) “An Introduction to Modern Bayesian Econometrics”. Blackwell, Oxford.
119. Lauritzen, S. L. (1996) “Graphical Models”. 1st edn. Oxford Science Press, Oxford.
120. Lauritzen, S. L. and Speigelhalter, D. J. (1988) “Local computation with probabilities on graphical structures and their application to expert systems (with discussion)”. Journal of the Royal Statistical Society B 50, 157–224.
121. Lauritzen, S. L. and Wermuth, N. (1989) “Graphical models for associations between variables, some of which are qualitative and some quantitative”. Annals of Statistics 17, 31–57.
122. Laurizen, S. L., Dawid, A. P., Larsen, B. N. and Leimer, H.-G. (1990) “Independence properties of directed Markov fields”. Networks 20, 491–505.
123. Levinas, E. (1969) “Totality and Infinity: An Essay on Exteriorarity”. Duquesne University Press, Pittsburgh.
124. Lindley, D. V. (1988) “Reconciliation of discrete probability distributions”. In: Bayesian Statistics 2 (eds. J. M. Bernado et al.). North-Holland, Amsterdam, pp. 375–390.
125. Little, R. J. A. and Rubin, D. B. (2002) “Statistical Analysis with Missing Data”. 2nd edn. Wiley, Chichester.
126. Liverani, S., Anderson, P. E., Edwards, K. D., Millar, A. J. and Smith, J. Q. (2008) “Efficient utility-based clustering over high dimensional partition spaces”. Journal of Bayesian Analysis 04(03), 539–572.
127. Louchard, J., Schneider, T. and French, S. (1992) “International Chernobyl Project: Summary Report of Decision Conferences held in the USSR October - November 1990”. Luxemburg City, European Commission.
128. Lui, J. and Hodges, J. S. (2003) “Posterior bimodality in the balanced one-way random effects model”. Journal of the Royal Statistical Society B 65(1), 247–256.
129. Lukacs, E. (1965) “A characterisation of the gamma distribution”. Annals of Mathematical Statistics 26, 319–324.
130. Marin, J. M. and Robert, C. P. (2007) “Bayesian Core: A Practical Approach to Computational Bayesian Analysis”. Springer-Verlag, New York.
131. Marin, J. M., Mengersen, K. and Robert, C. P. (2004) “Bayesian modelling and inference on mixtures of distributions”. In: Handbook of Statistics 25 (eds. D. Dey and C. R. Rao). Elsevier-Science, Amsterdam.
132. Marshall, K. T. and Oliver, R. M. (1995) “Decision Making and Forecasting”. McGraw-Hill, New York.
133. McAllister, D., Collins, M. and Periera, F. (2004) “Case factor diagrams for structured probability modelling”. In: Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI-04), pp. 382–391.
134. McLish, D. K. and Powell, S. H. (1989) “How well can physicians estimate mortality in a medical intensive care unit?”. Medical Decision Making 9, 125–132.
135. Meek, C. (1995) “Strong completeness and faithfulness in Bayesian networks”. In: Uncertainty in Artificial Intelligence 11 (eds. P. Besnard and S. Hanks). Morgan Kaufmann, New York, pp. 403–418.
136. Meyer, R. F. (1970) “On the relationship among the utility of assets, the utility of consumption and investment strategy in an uncertain but time invariant world”. In: OR 69 Proceedings of the Fifth Conference in Operations Research (ed. J. Lawrence). Tavistock Publications, London.
137. Mond, D. M. Q., Smith, J. Q. and Van Straten, D. (2003) “Stochastic factorisations, sandwiched simplices and the topology of the space of explanations”. Proceedings of the Royal Society of London A 459, 2821–2845.
138. Murphy, A. H. and Winkler, R. L. (1977) “Reliability of subjective probability forecasts of precipitation and temperature: some preliminary results”. Applied Statistics 26, 41–47.
139. Neil, M., Tailor, M., Marquez, D., Fenton, N. E. and Hearty, P. (2008) “Modelling dependable systems using hybrid Bayesian networks”. Reliability Engineering and System Safety 93(7), 933–939.
140. Nodelman, U., Shelton, C. R. and Koller D. (2002) “Continuous time Bayesian networks”. Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 378–387.
141. O’Hagan, A. (1979) “On outlier rejection phenomena in Bayesian inference”. Journal of the Royal Statistical Society B 41, 358–367.
142. O’Hagan, A. (1988) “Probability: Methods and Measurements”. Chapman and Hall, London.
143. O’Hagan, A. and Forster, J. (2004) “Bayesian Inference”. In: Kendall’s Advanced Theory of Statistics. Arnold, London.
144. O’Hagan, A., Buck, C. E., Daneshkhah, A., et al. (2006) “Uncertain Judgements: Eliciting Experts’ Probabilities”. Wiley, Chichester.
145. Oaksford, M. and Chater, N. (2006) “Bayesian Rationality”. Oxford, Oxford University Press.
146. Oaksford, M. and Chater, N. (eds.) (1998) “Rational Models of Cognition”. Oxford University Press.
147. Oliver, R. M. and Smith, J. Q. eds (1990) “Influence Diagrams, Belief Nets, and Decision Analysis”. Wiley, Chichester.
148. Olmsted, S. M. (1983) “On representing and solving decision problems”. Ph.D. dissertation. Engineering-Economic Systems, Stanford University.
149. Papamichail, K. N. and French, S. (2003) “Explaining and justifying the advice of a decision support system: a natural language generation approach”. Expert Systems with Applications 24(1), 35–48.
150. Papamichail, K. N. and French, S. (2005) “Design and evaluation of an intelligent decision support system for nuclear emergencies”. Decision Support Systems 41(1), 84–111.
151. Papaspiliopoulos, O. and Roberts, G. (2009) “Stability of the Gibbs sampler for Bayesian hierarchical models”. Annals of Statistics 36(1), 95–117.
152. Pearl, J. (1988) “Probabilistic Reasoning in Intelligent Systems”. Morgan Kauffman, San Mateo.
153. Pearl, J. (1995) “Causal diagrams for empirical research”. Biometrika 82, 669–710.
154. Pearl, J. (2000) “Causality, models, reasoning and inference”. Cambridge University Press.
155. Pearl, J. (2003) “Statistics and causal inference: a review (with discussion)”. Sociedad de Estadistica e Investigacion Operativa Test 12, 2, 281–345.
156. Peterka, V. (1981) “Bayesian system identification”. In: Trends and Progress in System Identification (ed. P. Eykhoff). Pergamon Press, Oxford, pp. 239–304.
157. Phillips, L. D. (1984) “A theory of requisite decision models”. Acta Psychologia 56, 29–48.
158. Phillips, L. D. (2007) “Decision conferencing”. In: Advances in Decision Analysis: From Foundations to Applications (eds. W. Edwards, R. F. Miles Jr., D. von Winterfeldt). Cambridge University Press, pp. 375–399.
159. Pollack, R. A. (1967) “Additive von Neumann-Morgenstern utility functions”. Econometrica 35, 485–494.
160. Poole, D. and Zhang, N. L. (2003) “Exploiting contextual independence in probabilistic inference”. Journal of Artificial Intelligence Research 18, 263–313.
161. Puch, R. O. and Smith, J. Q. (2004) “FINDS: A Training Package to Assess Forensic Fibre Evidence”. In: Advances in Artificial Intelligence. Springer-Verlag, New York, pp. 420–429.
162. Puch, R. O., Smith, J. Q. and Bielza, C. (2004) “Inferentially efficient propagation in non- decomposable Bayesian networks with hierarchical junction trees”. In: Advances in Bayesian Networks. Physica-Verlag, New York, pp. 57–74.
163. Queen, C. M. and Smith, J. Q. (1992) “Symmetric dynamic graphical chain models”. In: Bayesian Statistics 4 (eds. J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith). Oxford University Press, pp. 741–751.
164. Queen, C. M. and Smith, J. Q. (1993) “Multi-regression dynamic models”. Journal of the Royal Statistical Society B 55(4), 849–870.
165. Queen, C. M., Smith, J. Q. and James, D. M. (1994) “Bayesian forecasts in markets with overlapping structures”. International Journal of Forecasting 10, 209–233.
166. Raiffa, H. (1968) “Decision Analysis”. Addison-Wesley, Reading, MA.
167. Raiffa, H. and Schlaifer, R. (1961) “Applied Statistical Decision Theory”. MIT Press, Cambridge, MA.
168. Ramsey, F. P. (1931) “The Foundations of Mathematics and other Essays”. Routledge and Kegan Paul, London.
169. Ranyard, D. C. and Smith, J. Q. (1997) “Building a Bayesian model in a scientific environment: managing uncertainty after an accident”. In: The Practice of Bayesian Analysis (eds. S. French, J. Q. Smith). Arnold, London, pp. 245–258.
170. Rasmussen, C. E. and Williams, C. K. I. (2006) “Gaussian Processes for Machine Learning”. MIT Press, Cambridge, MA.
171. Renooij, S. (2001) “Probability elicitation for belief networks: issues to consider”. Knowledge Engineering Review 16(3), 255–269.
172. Riccomagno, E. and Smith, J. Q. (2005) “The Causal Manipulation and Bayesian Estimation of Chain Event Graphs”. CRiSM Research Report. CRiSM, University of Warwick.
173. Riccomagno, E. M. and Smith, J. Q. (2004) “Identifying a cause in models which are not simple Bayesian networks”. Proceedings of IMPU, Perugia, July 2004, pp. 1315–1322.
174. Riccomagno, E. and Smith, J. Q. (2009) “The geometry of causal probability trees that are algebraically constrained”. In: Optimal Design and Related Areas in Optimization and Statistics (eds. L. Pronzato and A. Zhigljavsky). Springer-Verlag, New York, pp. 131–152.
175. Richardson, T. S. and Spirtes, P. (2202) “Ancestral graph Markov models”. Annals of Statistics 30, 962–1030.
176. Rigat, F. and Smith, J. Q. (2009) “Non-parametric dynamic time series modelling with applications to detecting neural dynamics”. Annals of Applied Statistics 3(4), 1776–1804.
177. Robert, C. (2001) “The Bayesian Case”. 2nd edn. Springer-Verlag, Berlin.
178. Robert, C. P. and Casella, G. (2004) “Monte Carlo Statistical Methods”. 2nd edn. Springer- Verlag, New York.
179. Robins, J. M. (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 models in medicine: diseases and epidemics, Part 2.] Mathematical Modelling 7(9–12), 1393–1512.
180. Robins, J. M. (1997) “Causal inference from complex longitudinal data”. In: Latent Variable Modeling and Applications to Causality (ed. M. Berkane). Springer-Verlag, New York, pp. 69–117.
181. Robins, J. M., Scheines, R., Spirtes, P. and Wasserman, L. (2003) “Uniform consistency in causal inference”. Biometrika 90(3), 491–515.
182. Rossi, P. E., Allenby, G. M. and McCulloch, R. (2005) “Bayesian Statistics and Marketing”. Wiley, Chichester.
183. Rubin, D. B. (1978) “Bayesian inference for causal effects: the role of randomisation”. Annals of Statistics 6, 34–58.
184. Salmaron, A., Cano, A. and Moral, S. (2000) “Importance sampling in Bayesian networks using probability trees”. Computational Statistics and Data Analysis 24, 387–413.
185. Santos, A. A. F. (2002) “A dynamic Bayesian analysis in statistical models used with certain financial risk problems”. PhD thesis, University of Warwick.
186. Schervish, M. J. (1995) “The Theory of Statistics”. Springer-Verlag, New York.
187. Shachter, R. D. (1986) “Evaluating influence diagrams”. Operations Research 34, 871–882.
188. Shafer, G. (1976) “A Mathematical Theory of Evidence”. Princeton University Press.
189. Shafer, G. R. (1996) “The Art of Causal Conjecture”. MIT Press, Cambridge, MA.
190. Shafer, G. and Vovk, V. (2001) “Probability and Finance: It’s Only a Game!”. Wiley, Chichester.
191. Shafer, G. R., Gillett, P. R. and Scherl, R. (2000) “The logic of events”. Annals of Mathematics and Artificial Intelligence 28, 315–389.
192. Silander, T., Kontkanen, P. and Myllymaki, P. (2007) “On sensitivity of the MAP Bayesian network structure to the equivalent sample size parameter”. In: Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (eds. R. Parr and L. van der Gaag). AUAI Press, pp. 360–367.
193. Small, C. G. and McLeish, D. L. (1994) “Hilbert Space Methods in Probability and Statistical Inference”. John Wiley and Sons, Chichester.
194. Smith, J. Q. (1977) “Problems in Bayesian statistics relating to discontinuous phenomena, catastrophe theory and forecasting”. PhD Thesis, Warwick University.
195. Smith, J. Q. (1979a) “A generalisation of the Bayesian steady forecasting model”. Journal of the Royal Statistical Society B 41, 375–387.
196. Smith, J. Q. (1979b) “Mixture catastrophes and Bayes decision theory”. Mathematical Proceedings of the Cambridge Philosophical Society 86, 91–101.
197. Smith, J. Q. (1980a) “Bayes estimates under bounded loss”. Biometrika 67(3), 629–638.
198. Smith, J. Q. (1980b) “The prediction of prison riots”. British Journal of Mathematical and Statistical Psychology 33, 151–160.
199. Smith, J. Q. (1981) “Search effort and the detection of faults”. British Journal of Mathematical and Statistical Psychology 34, 181–193.
200. Smith, J. Q. (1983) “Forecasting accident claims in an assurance company”. Statistician 32, 109–115.
201. Smith, J. Q. (1985) “Diagnostic checks of non-standard time series models”. Journal of Forecasting 4, 283–291.
202. Smith, J. Q. (1988a) “Decision Analysis: A Bayesian Approach”. Chapman and Hall, London.
203. Smith, J. Q. (1988b) “Models, Optimal Decisions and Influence Diagrams”. In: Bayesian Statistics 3 (eds. J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith). Oxford University Press, pp. 765–776.
204. Smith, J. Q. (1989a) “Influence diagrams for Bayesian decision analysis”. European Journal of Operational Research 40, 363–376.
205. Smith, J. Q. (1989b) “Influence diagrams for statistical modelling”. Annals of Statistics 17, 654–672.
206. Smith, J. Q. (1990) “Statistical principles on graphs” (with discussion). In: Influence Diagrams, Belief Nets and Decision Analysis (eds. J. Q. Smith and R. M. Oliver). Wiley, Chichester, pp. 89–120.
207. Smith, J. Q. (1992) “A comparison of the characteristics of some Bayesian forecasting models”, International Statistical Reviews, 60, 1, 75–87.
208. Smith, J. Q. (1994) “Decision influence diagrams and their uses”. In: Decision Theory and Decision Analysis: Trends and Challenges (ed. S. Rios). Kluwer, Dordrecht, pp. 32–51.
209. Smith, J. Q. (1996) “Plausible Bayesian games”. Bayesian Statistics 5 (eds. J. M. Bernardo et al.). Oxford University Press, pp. 551–560.
210. Smith, J. Q. (2007) “Local Robustness of Bayesian Parametric Inference and Observed Likelihoods”. CRiSM Research Report 07–09. CRiSM, University of Warwick.
211. Smith, J. Q. and Allard, C. T. J. (1996) “Rationality, conditional independence and statistical models of competition”. In: Computational Learning and Probabilistic Reasoning (ed. A. Gammerman). Wiley, Chichester, pp. 237–256.
212. Smith, J. Q. and Anderson, P. E. (2008) “Conditional independence and chain event graphs”. Artificial Intelligence 172(1), 42–68.
213. Smith, J. Q. and Croft, J. (2003) “Bayesian networks for discrete multivariate data: an algebraic approach to inference”. Journal of Multivariate Analysis 84, 387–402.
214. Smith, J. Q. and Daneshkhah, A. (2009) “On the robustness of Bayesian networks to learning from non-conjugate sampling”. International Journal of Approximate Reasoning 51, 558–572.
215. Smith, J. Q. and Faria, A. E. (2000) “Bayesian Poisson models for the graphical combination of dependent expert information”. Journal of the Royal Statistical Society B 62(3), 525–544.
216. Smith, J. Q. and Figueroa-Quiroz, L. J. (2007) “A causal algebra for dynamic flow networks”. In: Advances in Probabilistic Graphical Models (eds. P. Lucas, J. A. Gamez and A. Salmeron). Springer-Verlag, New York, pp. 39–54.
217. Smith, J. Q. and French, A. (1993) “Bayesian updating of atmospheric dispersion models for use after an accidental release of radio-activity”. The Statistician 42(5), 501–511.
218. Smith, J. Q. and Papamichail, K. N. (1999) “Fast Bayes and the dynamic junction forest”. Artificial Intelligence 107, 99–124.
219. Smith, J. Q. and Queen, C. M. (1996) “Bayesian models for sparse probability tables”. Annals of Statistics 24(5), 2178–2198.
220. Smith, J. Q. and Rigat, F. (2009) “Isoseparation and Robustness in Finite Parameter Bayesian Inference”. CRISM Research Report 07–22. CRiSM, University of Warwick.
221. Smith, J. Q. and Thwaites, P. (2008a) “Decision trees”. In: Encyclopaedia of Quantitative Risk Analysis and Assessment, 2 (eds. E. L. Melnick and B. S. Everitt). Wiley, Chichester, pp. 462–470.
222. Smith, J. Q. and Thwaites, P. (2008b) “Influence Diagrams”. In: Encyclopedia of Quantitative Risk Analysis and Assessment 2 (eds. E. L. Melnick and B. S. Everitt). Wiley, Chichester.
223. Smith, J. Q., Faria, A. E., French, S. et al. (1997) “Probabilistic data assimilation with RODOS”. Radiation Protection Dosimetry 73(1-4), 57–59.
224. Smith, J. Q., Harrison, P. J. and Zeeman, E. C. (1981) “The analysis of some discontinuous decision processes”. European Journal of Operations Research 7(1), 30–43.
225. Smith, J. Q., Anderson, P. E. and Liverani, S. (2008a) “Separation measures and the geometry of Bayes factor selection for classification”. Journal of the Royal Statistical Society B 70, Part 5, 957–980.
226. Smith, J. Q., Dodd, L. and Moffat, J. (2008b) “Devolving Command under Conflicting Military Objectives”. CRiSM Research Report 08–09. CRiSM, University of Warwick.
227. Speigelhalter, D. and Lauritzen, L. (1990) “Sequential updating of conditional probabilities on directed graphical structures”. Networks 20, 579–605.
228. Speigelhalter, D. J. and Knill-Jones, R. P. (1984) “Statistical knowledge-based approaches to clinical decision support systems with applications to gastroenterology”. Journal of the Royal Statistical Society A 147, 35–77.
229. Spiegelhalter, D. J., David, A. P., Lauritzen, S. L. and Cowell, R. G. (1993) “Bayesian analysis in expert systems” (with discussion). Statistical Science 8, 219–263.
230. Spirtes, P., Glymour, C. and Scheines, R. (1993) “Causation, Prediction, and Search”. Springer-Verlag, New York.
231. Steck, H. (2008) “Learning the Bayesian network structure: Dirichlet prior versus data”. In: UAI, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (eds. D. A. McAllester and P. Myllymaki). AUAI Press, pp. 511–518.
232. Steck, H. and Jaakkola, T. (2002) “On the Dirichlet prior and Bayesian regularization”. In: Advances in Neural Information Processing Systems [Neural Information Processing Systems, NIPS] (eds. S. Becker, S. Thrun and K. Obermayer). MIT Press, Cambridge, MA, 697–704 27.
233. Studeny, M. (1992) “Conditional independence relations have no finite complete characterization”. In: Information Theory, Statistical Decision Functions and Random Processes Transactions of 11th Prague Conference, Vol B (eds. S. Kubik and J. A. Visek). Kluwer, Dordrecht, pp. 377–396.
234. Studeny, M. (2005) “Probabilistic Conditional Independence Structures”. Springer-Verlag, New York.
235. Tarjan, R. and Yannakakis, M. (1984) “Simple linear time algorithms to test chordality of graphs, test acyclicity of hypergraphs and selectively reduce acyclic hypergraphs”. SIAM Journal of Computing 13, 566–579.
236. Tatman, A. and Shachter, R. D. (1990) “Dynamic programming and influence diagrams”. IEEE Transactions on Systems, Man and Cybernetics 20(2), 365–379.
237. Thwaites, P., Smith, J. Q. and Cowell, R. (2008) “Propagation using chain event graphs”. In: Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (eds. D. McAllester and P. Myllymaki), Helsinki, July 2008. AUAI Press, pp. 546–553.
238. Thwaites, P., Smith, J. Q. and Riccomagno, E. (2010) “Causal analysis with chain event graphs”. Artificial Intelligence 174, 889–909.
239. Tian, J. (2008) “Identifying dynamic sequential plans”. Proceedings of the 24th Annual Conference on Uncertainty in Artificial Intelligence (UAI-08), pp. 554–561.
240. Verma, T. and Pearl, J. (1988) “Causal networks: semantics and expressiveness”. In: Uncertainty in Artificial Intelligence IV (eds. R. D. Schachter et al.). North-Holland, Amsterdam, pp. 69–76.
241. Verma, T. and Pearl, J. (1990) “Equivalence and synthesis of causal models”. In: Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence (eds P. Bonisson et al.). North-Holland, Amsterdam, pp. 255–270.
242. Vomelelova, M. and Jensen, F. V. (2002) “An extension of lazy evaluation for influence diagrams avoiding redundant variables in the potentials”. In: Proceedings of the First European Workshop on Probabilistic Graphical Models (eds. J. A. Gamez and A. Salmeron). Cuenca, Spain.
243. von Winterfeldt, D. and Edwards, W. (1986) “Decision Analysis and Behavioural Research”. Cambridge University Press.
244. Wakefield, J. C., Zhou, C. and Self, S. F. (2003) “Modelling gene expression over time: curve clustering with informative prior distributions”. In: Bayesian Statistics 7 (eds. J. M. Bernardo et al.). Oxford University Press, pp. 721–732.
245. Walley, P. (1991) “Statistical Reasoning and Imprecise Probabilities”. Chapman and Hall, London.
246. Wasserman, L. (1992) “Invariance properties of density ratio priors”. Annals of Statistics 20,2177–2182.
247. Wasserman, L. (1996) “The conflict between improper priors and robustness”. Journal of Statistical Planning and Inference 52(1), 1–15.
248. West, M. and Harrison, P. J. (1997) “Bayesian Forecasting and Dynamic Models”. Springer- Verlag, New York.
249. Whittaker, J. (1990) “Graphical Models on Applied Multivariate Statistics”. Wiley, Chichester.
250. Whittle, P. (1990) “Risk sensitive optimal control”. Wiley, Chichester.
251. Wilkie, M. E. and Pollack, A. C. (1996) “An application of probability judgement accuracy measures to currency forecasting”. International Journal of Forecasting 12, 25–40.
252. Winkler, R. L. (1986) “Expert resolution”. Management Science 32, 298–303.
253. Wright, G., Rowe, G., Bolger, F. and Gammack, J. (1994) “Coherence, calibration and expertise in judgemental probability forecasting”. Human Decision Processes 57, 1–25.
254. Xiang, Y. (2002) “Probabilistic Reasoning in Multiagent Systems”. Cambridge University Press.
255. Yates, J. F. and Curley, S. P. (1985) “Conditional distribution analyses of probabilistic forecasts”. Journal of Forecasting 4, 61–73.