Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved

Expert Systems with Applications - Tập 56 - Trang 197-208 - 2016
Anthony Costa Constantinou1, Norman Fenton1,2, Martin Neil1,2
1Risk and Information Management (RIM) Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
2Agena Ltd., Cambridge CB23 7NU, UK

Tài liệu tham khảo

Andreassen, 1999, Using probabilistic and decision theoretic methods in treatment and prognosis modelling, Artificial Intelligence in Medicine, 15, 121, 10.1016/S0933-3657(98)00048-7 Bergus, 1995, Clinical reasoning about new symptoms despite pre-existing disease: Sources of error and order effects, Family Medicine Journal, 27, 314 Bishop, 2006 Chaloner, 1996, Elicitation of prior distributions, 141 Constantinou, 2012, pi-football: A Bayesian network model for forecasting association football match outcomes, Knowledge-Based Systems, 36, 339 Constantinou, 2013, Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks, Knowledge-Based Systems, 50, 60, 10.1016/j.knosys.2013.05.008 Constantinou, 2016, Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences, Artificial Intelligence in Medicine, 66, 41, 10.1016/j.artmed.2015.09.002 Constantinou, 2015, Risk assessment and risk management of violent reoffending among prisoners, Expert Systems with Applications, 42, 7511, 10.1016/j.eswa.2015.05.025 Constantinou, A., & Fenton, N. (2016). Improving predictive accuracy using smart-data rather than big-data: A case study of football teams' evolving performance. (Submitted for publication), 2016. de Campos, 2011, Efficient structure learning of Bayesian networks using constraints, Journal of Machine Learning Research, 12, 663 Druzdzel, 1995, Elicitation of probabilities for belief networks: Combining qualitative and quantitative information, 141 Enders, 2006, A primer on the use of modern missing-data methods in psychosomatic medicine research, Psychosomatic Medicine, 68, 427, 10.1097/01.psy.0000221275.75056.d8 Evans, 1985, Prior beliefs and statistical inference, British Journal of Psychiatry, 76, 469 Evans, 2002, Background beliefs in Bayesian inference, Memory & Cognition, 30, 179, 10.3758/BF03195279 Fenton, 2012 Fenton, 2007, Using Ranked nodes to model qualitative judgments in Bayesian networks, IEEE Transactions on Knowledge and Data Engineering, 19, 1420, 10.1109/TKDE.2007.1073 Friedman, 1997, Bayesian network classifiers, Machine Learning, 29, 131, 10.1023/A:1007465528199 Friedman, 2000, Using Bayesian networks to analyze expression data, Journal of Computational Biology, 7, 601, 10.1089/106652700750050961 Gigerenzer, 1995, How to improve Bayesian reasoning without instruction: Frequency formats, Psychological Review, 102, 684, 10.1037/0033-295X.102.4.684 Heckerman, 1992, Towards normative expert systems. I. The pathfinder project, Methods of Information in Medicine, 31, 90, 10.1055/s-0038-1634867 Heckerman, 1992, Towards normative expert systems. II. Probability-based representations for efficient knowledge acquisition and inference, Methods of Information in Medicine, 31, 106, 10.1055/s-0038-1634868 Hofvind, 2012, False-positive results in mammographic screening for breast cancer in Europe: A literature review and servey of service screening programmes, Journal of Medical Screening, 19, 57, 10.1258/jms.2012.012083 Hughes, 1991, Practical reporting of Bayesian analyses of clinical trials, Drug Information Journal, 25, 381, 10.1177/009286159102500308 Hunter, 2004, A tutorial on MM algorithms, The American Statistician, 58, 30, 10.1198/0003130042836 Jaakkola, 2010, Learning Bayesian network structure using LP relaxations, 358 Jamshidian, 1997, Acceleration of the EM algorithm by using Quasi-Newton methods, Journal of the Royal Statistical Society, Series B, 59, 569, 10.1111/1467-9868.00083 Jiangtao, 2012, Accelerating expectation-maximization algorithms with frequent updates Johnson, 2010, Methods to elicit beliefs for Bayesian priors: A systematic review, Journal of Clinical Epidemiology, 63, 355, 10.1016/j.jclinepi.2009.06.003 Johnson, 2010, A valid and reliable belief elicitation method for Bayesian priors, Journal of Clinical Epidemiology, 63, 370, 10.1016/j.jclinepi.2009.08.005 Jordan, 1999 Kendrick, 2015 Korb, 2011 Kuipers, 1988, Critical decisions under uncertainty: Representation and structure, Cognitive Science, 12, 177, 10.1207/s15516709cog1202_2 Kullback, 1959 Kullback, 1951, On information and sufficiency, Annals of Mathematical Statistics, 22, 79, 10.1214/aoms/1177729694 Lauritzen, 1995, The EM algorithm for graphical association models with missing data, Computational Statistics & Data Analysis, 19, 191, 10.1016/0167-9473(93)E0056-A Li, 2005, Experimental tests of subjective Bayesian methods, The Psychological Record, 55, 251, 10.1007/BF03395509 Lucas, 2000, A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU, Artificial Intelligence in Medicine, 19, 251, 10.1016/S0933-3657(00)00048-8 Matsuyama, 2003, The α-EM algorithm: Surrogate likelihood maximization using α-logarithmic information measures, IEEE Transactions on Information Theory, 49, 692, 10.1109/TIT.2002.808105 Murphy, 1977, Reliability of subjective probability forecasts of precipitation and temperature, Journal of Applied Statistics, 26, 41, 10.2307/2346866 Nassif, 2013, Score As You Lift (SAYL): A statistical relational learning approach to uplift modeling, 595 Nassif, 2012, Logical differential prediction bayes net, improving breast cancer diagnosis for older women, 1330 Neil, 2007, Inference in hybrid Bayesian networks using dynamic discretization, Statistics and Computing, 17, 219, 10.1007/s11222-007-9018-y Normand, 2002, Using elicitation techniques to estimate the value of ambulatory treatments for major depression, Medical Decision Making, 22, 245, 10.1177/0272989X0202200313 O'Hagan, 2006 O'Hagan, 1998, Eliciting expert beliefs in substantial practical application, Statistician, 47, 21, 10.1111/1467-9884.00114 Pearl, 2009 Petitjean, 2013, Scaling log-linear analysis to high-dimensional data Rebonato, 2010 Renooij, 2001, Probability elicitation for belief networks: Issues to consider, Knowledge Engineering Review, 16, 255, 10.1017/S0269888901000145 Spiegelhalter, 2004 Spirtes, 1991, An algorithm for fast recovery of sparse causal graphs, Social Science Computer Review, 9, 62, 10.1177/089443939100900106 Spirtes, 1993 Van der Fels-Klerx, 2002, Elicitation of quantitative data from a heterogeneous expert panel: Formal process and application in animal health, Risk Analysis, 22, 67, 10.1111/0272-4332.t01-1-00007 van der Gaag, 1999, How to elicit many probabilities, 647 van der Gaag, 2002, Probabilities for a probabilistic network: A case study in oesophageal cancer, Artificial Intelligence in Medicine, 25, 123, 10.1016/S0933-3657(02)00012-X Verma, 1991, Equivalence and synthesis of causal models, 255 Wallsten, 1983, Encoding subjective probabilities: A psychological and psychometric review, Management Science, 29, 151, 10.1287/mnsc.29.2.151 White, 2005, Eliciting and using expert opinions about influence of patient characteristics on treatment effects: A Bayesian analysis of the CHARM trials, Statistics in Medicine, 24, 3805, 10.1002/sim.2420 Yet, 2013, Decision support system for Warfarin therapy management using Bayesian networks, Decision Support Systems, 55, 488, 10.1016/j.dss.2012.10.007 Yet, 2015, Project cost, Benefit and Risk Analysis using Bayesian Networks Zhou, 2014, Bayesian network approach to multinomial parameter learning using data and expert judgments, International Journal of Approximate Reasoning, 55, 1252, 10.1016/j.ijar.2014.02.008 Zhou, 2014, An extended MPL-C model for Bayesian network parameter learning with exterior constraints, 581