Aitken, C. G. G. and F. Taroni (2004 ). Statistics and the evaluation of evidence for forensic scientists (2nd Edition), John Wiley & Sons, Ltd.
 Altendorf, E. E., ‘Learning from sparse data by exploiting monotonicity constraints’, in Conf. Uncertainty in Artificial Intelligence, 2005, pp. 18–26.
 Balding, D. J. (2005). Weight-of-Evidence for Forensic DNA Profiles, Wiley.
 Barber, D. (2012). Bayesian Reasoning and Machine Learning. New York, Cambridge University Press.
 Berger, C. E. H., J. Buckleton, C. Champod, I. Evett and G. Jackson (2011). "Evidence evaluation: A response to the court of appeal judgement in R v T." Science and Justice 51: 43-49.
 Bezemer, D. J. (2009). "No One Saw This Coming: Understanding Financial Crisis Through Accounting Models." MPRA Paper No. 15892
 BIAS Project http://www.bias-project.org.uk
 Biedermann, A. and F. Taroni (2006). "Bayesian networks and probabilistic reasoning about scientific evidence when there is a lack of data." Forensic Science International 157(2): 163-167.
 Boylan, JF, BP Kavanagh, and J Armitage (2011), Randomised controlled trials: important but overrated?, J R Coll Physicians Edinb 2011; 41:126–31
 Broeders, T. (2009). Decision-Making in the Forensic Arena. In “Legal Evidence and Proof: Statistics, Stories and Logic”. (Eds H. Kaptein, H. Prakken and B. Verheij, Ashgate) 71-92.
 BUGS Project - Bayesian inference Using Gibbs Sampling http://www.mrc-bsu.cam.ac.uk/bugs/
 Buntine W., ‘Theory Refinement on Bayesian Networks’, in Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91), San Mateo, CA, 1991, pp. 52–60.
 Cano A., Masegosa A. R., and Moral S., ‘A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data’, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 41, no. 5, pp. 1382–1394, 2011.
 Cartwright, N. and E. Munro (2010). "The limitations of randomized controlled trials in predicting effectivenessjep_1382 260..266." Journal of Evaluation in Clinical Practice 16: 260-266.
 Cheng J., Bell D. A., and Liu W., ‘Learning belief networks from data: an information theory based approach’, in Proceedings of the sixth international conference on Information and knowledge management, New York, NY, USA, 1997, pp. 325–331.
 Constantinou, A., N. E. Fenton and M. Neil (2012). ""pi-football: A Bayesian network model for forecasting Association Football match outcomes." Knowledge Based Systems, 36, 322-339 http://dx.doi.org/10.1016/j.knosys.2012.07.008
 Cooper, G. F. and Herskovits E., ‘A Bayesian method for the induction of probabilistic networks from data’, Machine Learning, vol. 9, no. 4, pp. 309–347, 1992.
 Cooper., G. F. (1990). "The computational complexity of probabilistic inference using bayesian belief networks ." Artificial Intelligence 42(2-3): 393-405.
 Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks Cambridge University Press.
 Darwiche, A. (2003). A differential approach to inference in Bayesian networks. J. ACM, 50, 280–305
 Dawid, A. P., J. Mortera and P. Vicard (2007). "Object-oriented Bayesian networks for complex forensic DNA profiling problems." Forensic Science International 169: 195-205.
 de Campos C. P. and Ji Q., ‘Improving Bayesian Network parameter learning using constraints’, in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, 2008, pp. 1 –4.
 Druzdzel MJ, Henrion M. Efficient reasoning in qualitative probabilistic networks. In: Proceedings of the 11th Annual Conference on Artificial Intelligence (AAAI-93). Washington, DC, July 11–15. 1993;p. 548–553
 Druzdzel, M. K. and L. C. van der Gaag (2000). "Building Probabilistic Networks: Where Do the Numbers Come From?" IEEE Transactions on Knowledge and Data Engineering 12(4): 481-486.
 Elidan, G. and N. Friedman (2005). "Learning hidden variable networks: The information bottleneck approach." Journal of Machine Learning Research 6(81-127).
 Esposito, F. Malerba, D. Semeraro, G. Kay, J., A comparative analysis of methods for pruning decision trees, IEEE Trans Pattern Analysis and Machine Intelligence, May 1997, 19(5), 476-491
 Evett, I. W. and B. S. Weir (1998). Interpreting DNA evidence : statistical genetics for forensic scientists, Sinauer Associates.
 Feelders A. and van der Gaag L. C., ‘Learning Bayesian network parameters under order constraints’, International Journal of Approximate Reasoning, vol. 42, no. 1–2, pp. 37–53, May 2006.
 Fenton NE, "A simple story illustrating why pure machine learning (without expert input) may be doomed to fail and totally unnecessary", 12 Nov 2012, www.eecs.qmul.ac.uk/~norman/papers/ml_simple_example.pdf
 Fenton NE and Neil M, ''Making Decisions: Using Bayesian Nets and MCDA'', Knowledge-Based Systems 14, 307-325, 2001.
 Fenton NE, Neil M, and Caballero JG, "Using Ranked nodes to model qualitative judgements in Bayesian Networks" IEEE TKDE 19(10), 1420-1432, Oct 2007
 Fenton, N. and Neil, M. (2010). "Comparing risks of alternative medical diagnosis using Bayesian arguments." Journal of Biomedical Informatics, 43: 485-495, http://dx.doi.org/10.1016/j.jbi.2010.02.004
 Fenton, N. E. (2011). "Science and law: Improve statistics in court." Nature 479: 36-37. Paper on Nature online website is here. http://dx.doi.org/10.1038/479036a
 Fenton, N. E., D. Lagnado and M. Neil (2012). "A General Structure for Legal Arguments Using Bayesian Networks." Cognitive Science. 10.1111/cogs.12004
 Fenton, N. E., M. Neil, and D. Lagnado “Modelling mutually exclusive causes in Bayesian networks”, 2012 http://www.eecs.qmul.ac.uk/~norman/papers/mutual_IEEE_format_version.pdf
 Fenton, N.E. and M. Neil, Risk Assessment and Decision Analysis with Bayesian Networks. 2012, London Chapman and Hall.
 Fenton, N.E. and Neil, M. (2011), 'Avoiding Legal Fallacies in Practice Using Bayesian Networks', Australian Journal of Legal Philosophy 36, 114-151, 2011 ISSN 1440-4982
 Fenton, N.E. and Neil, M.(2012), 'On limiting the use of Bayes in presenting forensic evidence', http://www.eecs.qmul.ac.uk/~norman/papers/likelihood_ratio.pdf
 Fenton, N.E. and Neil, M., Managing Risk in the Modern World: Bayesian Networks and the Applications, 1. 2007, London Mathematical Society, Knowledge Transfer Report. www.eecs.qmul.ac.uk/~norman/papers/lsm.pdf
 Fiot, C., G. A. P. Saptawati, A. Laurent and M. Teisseire (2008). "Learning Bayesian network structure from incomplete data without any assumption." Database Systems for Advanced Applications 4947: 408-423 still assumes data but fills in the gaps
 Flores, J., M., A. E. Nicholson, A. Brunskill, K. B. Korb and S. Mascaro (2011). "Incorporating expert knowledge when learning Bayesian network structure: a medical case study." Artificial Intelligence in Medicine 53(3): 181-204.
 Flyvbjerg, B. (2006). "From Nobel Prize to Project Management: Getting Risks Right." Project Management Journal 37(3): 5-15.
 Gelman, A. (2008). "Objections to Bayesian statistics." Bayesian Analysis 3(3): 445-467.
 Gigerenzer, G. (2002). Reckoning with Risk: Learning to Live with Uncertainty. London, Penguin Books.
 Heckerman D., Geiger D., and Chickering D. M., ‘Learning Bayesian networks: The combination of knowledge and statistical data’, Machine Learning, vol. 20, no. 3, pp. 197–243, 1995.
 Helsper E., Gaag L. van der, and Groenendaal F., ‘Designing a Procedure for the Acquisition of Probability Constraints for Bayesian Networks’, in Engineering Knowledge in the Age of the Semantic Web, vol. 3257, E. Motta, N. Shadbolt, A. Stutt, and N. Gibbins, Eds. Springer Berlin / Heidelberg, 2004, pp. 280–292.
 Jensen, F. V. and T. Nielsen (2007). Bayesian Networks and Decision Graphs, Springer-Verlag New York Inc.
 Joseph A, Fenton NE, Neil M, "Predicting football results using Bayesian Nets and other Machine Learning Techniques", Knowledge Based Systems, Volume 19, Issue 7, Pages 544-553, Nov 2006
 Kahneman, D. (2012). Thinking, Fast and Slow. London, Penguin Books.
 Khan O. Z., Poupart P., and Agosta J. M., ‘Automated Refinement of Bayes Networks; Parameters based on Test Ordering Constraints’, in NIPS, 2011, pp. 2591–2599.
 Koller, D. and A. Pfeffer (1997). Object-Oriented Bayesian Networks. Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI) Providence, Rhode Island, 1-3 Aug 1997, Morgan Kaufmann, San Francisco: 302-313.
 Korb, K. B. and A. E. Nicholson (2004). Bayesian Artificial Intelligence, CRC Press.
 Koski, T. and J. Noble (2009). Bayesian Networks: An Introduction. Chichester, Wiley.
 Kozlov AV, and Koller D, Nonuniform dynamic discretization in hybrid networks. In D Geiger and PP Shenoy (eds.), Uncertainty in Artificial Intelligence, 13: 314–325 (1997).
 Lagnado, D. A., N. E. Fenton and M. Neil (2012). "Legal idioms: a framework for evidential reasoning." Argument and Computation, 2012, http://dx.doi.org/10.1080/19462166.2012.682656
 Laskey K. B. and Mahoney S. M., ‘Network fragments: representing knowledge for constructing probabilistic models’, in Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, San Francisco, CA, USA, 1997, pp. 334–341.
 Laskey, K. B. and S. Mahoney. (2000). "Network Engineering for Agile Belief Network Models." IEEE Transactions on Knowledge and Data Engineering 12(4): 487-498.
 Laskey, K.B., MEBN: A Language for First-Order Bayesian Knowledge Bases, Artificial Intelligence, 172(2-3): 140-178, 2008.
 Lauritzen S. L. and Spiegelhalter D. J., ‘Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems’, Journal of the Royal Statistical Society. Series B (Methodological), vol. 50, no. 2, pp. 157–224, Jan. 1988.
 Liao W.and Ji Q., ‘Learning Bayesian network parameters under incomplete data with domain knowledge’, Pattern Recognition, vol. 42, no. 11, pp. 3046–3056, Nov. 2009.
 Madsen, A. L. (2007). Bayesian Networks And Influence Diagrams. New York, Springer Verlag.
 Managing Uncertainty in Complex Models http://www.mucm.ac.uk/
 Marsh W and Bearfield G, Representing parameterised fault trees using Bayesian networks. In Proceedings of the 26th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2007, Springer-Verlag, 2007.
 Marsh W and Bearfield G, Using Bayesian networks to model accident causation in the UK railway industry. International Conference on Probabilistic Safety Assessment and Management, PSAM7, Berlin, June 2004.
 Marsh WR, Bearfield G, Generalizing event trees using Bayesian networks, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 222(2), 105-114, June 2008
 Mason A, Richardson S, Best N. (2012). Two-pronged strategy for using DIC to compare selection models with non-ignorable missing responses. Bayesian Analysis. 7:109-146
 Mattr M. R. and Domingos P., ‘Learning with Knowledge from Multiple Experts’, in In ICML 20, 2003, pp. 624–631.
 McGrayne, S. B. (2011). The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy Yale University Press
 Microsoft MSBNx Bayesian Network Editor and Tool Kit: http://research.microsoft.com/adapt/MSBNx/
 Mittal, A. and A. Kassim (2007). Bayesian Network Technologies: Applications and Graphical Models.
 Morrison, G. M. (2012). "The likelihood ratio framework and forensic evidence in court: a response to RvT." International Journal of Evidence and Proof 16(1).
 Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning, University of California, Berkely. PhD Thesis, www.cs.ubc.ca/~murphyk/Thesis/thesis.pdf
 Murphy, K. P. (2005). “Software Packages for Graphical Models / Bayesian Networks”, www.cs.ubc.ca/~murphyk/Bayes/bnsoft.html
A listing of BN tools also appears here: www.eecs.qmul.ac.uk/~norman/projects/BN_tools.html.
 Neapolitan, R. E. (2004). Learning Bayesian Networks, Prentice Hall.
 Neil M, Fenton N, Forey S and Harris R, "Using Bayesian Belief Networks to Predict the Reliability of Military Vehicles", IEE Computing and Control Engineering J 12(1), 11-20, 2001
 Neil M., Fenton N., and Nielson L., ‘Building large-scale Bayesian networks’, Knowl. Eng. Rev., vol. 15, no. 3, pp. 257–284, Sep. 2000.
 Neil, M, Chen X, Fenton, N E, "Optimizing the Calculation of Conditional Probability Tables in Hybrid Bayesian Networks using Binary Factorization", to appear IEEE Transactions on Knowledge and Data Engineering, 24(7), 1306 - 1312, 2012
 Neil, M., M. Tailor and D. Marquez (2007). "Inference in hybrid Bayesian networks using dynamic discretization." Statistics and Computing 17(3): 219-233.
 Neil, M., Marquez, D. and Fenton, N. E. (2010). "Improved Reliability Modeling using Bayesian Networks and Dynamic Discretization." Reliability Engineering & System Safety, 95(4), 412-425
 Neil, M., Marquez, D., and Fenton, N., Using Bayesian Networks to Model the Operational Risk to Information Technology Infrastructure in Financial Institutions. Journal of Financial Transformation, 2008. 22: p. 131-138.
 Neil, M., Tailor, M., Marquez, D., Fenton, N.E., and Hearty, P., Modelling dependable systems using hybrid Bayesian networks. Reliability Engineering and System Safety, 2008. 93(7): p. 933-939.
 Niculescu R. S., Mitchell T. M., and Rao R. B., ‘Bayesian Network Learning with Parameter Constraints’, J. Mach. Learn. Res., vol. 7, pp. 1357–1383, Dec. 2006.
 O'Hagan, A., C. E. Buck, et al. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Chichester,
 Onisko, A., M. J. Druzdzel and H. Wasyluk (2000). Learning Bayesian Network Parameters from Small Data Sets: Application of Noisy-OR Gates. In Workshop on Bayesian and Causal Networks: From Inference to Data Mining, 12th European Conference on Artificial Intelligence (ECAI-2000), Berlin.
 Parmigiani, G. (2002). Modeling in Medical Decision Making: A Bayesian Approach John Wiley & Sons Inc.
 Pearl J., Causality: Models, Reasoning and Inference, 2nd ed. New York, NY, USA: Cambridge University Press, 2009.
 Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1988.
 Pitchforth Jegar, Mengersen Kerrie L. A proposed validation framework for expert elicited Bayesian Networks. Expert Syst. Appl. 40(1): 162-167 (2013). 2012
 Pourret, O., P. Naïm and B. Marcot, Eds. (2008). Bayesian Networks: A Practical Guide to Applications. Statistics in Practice. Chichester, John Wiley & Sons.
 Redmayne, M. (2001). Expert Evidence and Criminal Justice, Oxford University Press.
 Redmayne, M., P. Roberts, C. Aitken and G. Jackson (2011). "Forensic Science Evidence in Question." Criminal Law Review (5): 347-356.
 Renooij S., ‘Probability elicitation for belief networks: issues to consider’, Knowl. Eng. Rev., vol. 16, no. 3, pp. 255–269, Sep. 2001.
 Renooij, S. and L. C. van der Gaag (2008). Discrimination and its sensitivity in probabilistic networks. Proceedings of the Fourth Workshop on Probabilistic Graphical Models. M. Jaeger and T. D. Nielsen. Hirtshals: 241-248.
 Robertson, B., G. A. Vignaux and C. E. H. Berger (2011). "Extending the confusion about Bayes." The Modern Law Review 74(3): 444-455.
 Sakellaropoulos, G. C. and G. C. Nikiforidis (1999). "Development of a Bayesian Network for the prognosis of head injuries using graphical model selection techniques." Methods Inf Med 38(1): 37-42.
 Singh, M.and Valtorta M., ‘An algorithm for the construction of Bayesian network structures from data’, in Proceedings of the Ninth international conference on Uncertainty in artificial intelligence, San Francisco, CA, USA, 1993, pp. 259–265.
 Smith, J. (2010). Bayesian Decision Analysis: Principles and Practice. Cambridge, Cambridge University Press.
 Smith, J. Q. and P. E. Anderson (2008). "Conditional independence and chain event graphs." Artif. Intell. 172(1): 42-68.
 Soares, M. O., L. Bojke, et al. (2011). "Methods to elicit experts' beliefs over uncertain quantities: application to a cost effectiveness transition model of negative pressure wound therapy for severe pressure ulceration." Stat Med. 30(19): 2363-80. Recent relevant structural elicitation without data work:
 Spiegelhalter, D. J., K. R. Abrams and J. P. Myles (2004). Bayesian Approaches to Clinical Trials and Health-care Evaluation, John Wiley and Sons
 Stefanini, F. M. (2008). Eliciting expert beliefs on the structure of a Bayesian Network. PGM 08, The Fourth European Workshop on Probabilistic Graphical Models. Aalberg, Denmark.
 Su J., Zhang H., Ling C. X., and Matwin S., ‘Discriminative parameter learning for Bayesian networks’, in Proceedings of the 25th international conference on Machine learning, New York, NY, USA, 2008, pp. 1016–1023.
 Thompson, W. C., F. Taroni and C. G. G. Aitken (2003). "How the probability of a false positive affects the value of DNA evidence." Journal of Forensic Sciences 48(1): 47-54.
 van der Gaag, L. C., J. H. Bolt, W. L. A. Loeffen and A. R. W. Elbers (2010). Modelling patterns of evidence in Bayesian networks: a case-study in Classical Swine Fever. Computational Intelligence for Knowledge-based Systems Design, Lecture Notes in AI. New York, Springer. 6178: 675-684.
 Wellman MP. Fundamental concepts of qualitative probabilistic networks. Artif. Intell. 1990;44(3):257–303
 Williamson, J. (2005). Bayesian Nets and Causality: Philosophical and Computational Foundations. Oxford, Oxford University Press.
 Yet, B., Perkins Z.,Marsh, W., Fenton, N.E., "Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support", ProBioMed 11, Bled, Slovenia, July 2011 http://probiomed.cs.ru.nl/papers/yet.pdf
 Zhang N. L. and Poole D., ‘Exploiting causal independence in Bayesian network inference’, J. Artif. Int. Res., vol. 5, no. 1, pp. 301–328, Dec. 1996.
 "Big Data Issue" August 2012, Special Issue of Significance , a joint publication of the American Statistical Assocation and the Royal Satisyical Society, www.signficancemagazine.org
 Getoor, L, D. Koller, N. Friedman, A. Pfeffer, B. Taskar. "Probabilistic Relational Models". . In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning, 2007.