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School of Electronic Engineering and Computer Science

Professor Norman Fenton

Norman

Professor of Risk and Information Management

Email: n.fenton@qmul.ac.uk
Telephone: +44 20 7882 7860
Room Number: Peter Landin, CS 435
Website: http://www.eecs.qmul.ac.uk/~norman
Office Hours: Tuesday 16:00-18:00

Teaching

Risk and Decision-Making for Data Science and AI (Postgraduate)

This module provides a comprehensive overview of the challenges of risk assessment, prediction and decision-making covering public health and medicine, the law, government strategy, transport safety and consumer protection. Students will learn how to see through much of the confusion spoken about risk in public discourse, and will be provided with methods and tools for improved risk assessment that can be directly applied for personal, group, and strategic decision-making. The module also directly addresses the limitations of big data and machine learning for solving decision and risk problems.

Research

Research Interests:

Full and current details of my research and publications can be found on my home page.

My current research focuses primarily on quantitative risk assessment. This typically involves analysing and predicting the probabilities of unknown events using causal, probabilistic models (Bayesian networks).  This type of reasoning enables improved assessment by taking account of both statistical data where available and also expert judgment,  providing more powerful insights and better decision making than is possible from purely data-driven solutions. Hence, the approach can be summarized as 'smart data rather than big data'.  Applications include medicine, law and forensics (I have been an expert witness or consultant in many major criminal and civil cases), security, software reliability, transport safety and reliability, finance, and football prediction.  I have been Principle Investigator in multiple collaborative projects (details of my current and recent projects can be found here).

I have a special interest in raising public awareness of the importance of probability theory and Bayesian reasoning in everyday life (including how to present such reasoning in simple lay terms) and maintain a  blog,  twitter account and also a website dedicated to this. I have published 7 books and over 300 referred articles. My book "Risk Assessment and Decision Analysis using Bayesian Networks" with Martin Neil (first edition 2012, second edition 2018) was the first to bring Bayesian networks to a general audience. In 2015 I presented the award-winning BBC documentary Climate Change by Numbers.

In addition to my research on risk assessment, I have a long track record of work in software engineering (including pioneering work on software metrics); the third edition of my book ?Software Metrics: A Rigorous and Practical Approach? was published in November 2014. 

Publications

    • McLachlan S, Neil M, Dube K et al. (2022), Smart automotive technology adherence to the law: (de)constructing road rules for autonomous system development, verification and safety $nameOfConference


    • McLachlan S, Kyrimi E, Dube K et al. (2022), The Self-Driving Car: Crossroads at the Bleeding Edge of Artificial Intelligence and Law $nameOfConference

    • Hunte JL, Neil M, Fenton NE (2022), A causal Bayesian network approach for consumer product safety and risk assessment $nameOfConference


    • Hunte JL, Neil M, Fenton NE (2022), A causal Bayesian network approach for consumer product safety and risk assessment. $nameOfConference


    • McLachlan S, Kyrimi E, Dube K et al. (2022), Lawmaps: enabling legal AI development through visualisation of the implicit structure of legislation and lawyerly process $nameOfConference


    • Daley BJ, Ni’Man M, Neves MR et al. (2022), mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review $nameOfConference


    • Fenton N, Cruz N, Hahn U et al. (2021), Explaining away, augmentation, and the assumption of independence $nameOfConference


    • Hartmann M, Fenton N, Dobson R (2021), Using Bayesian networks to understand multiple sclerosis risk factor interactions $nameOfConference

    • Fenton N, Lagnado D (2021), Bayesianism: Objections and Rebuttals $nameOfConference


    • McLachlan S, Neil M, Dube K et al. (2021), Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety $nameOfConference

    • Neil M, Fenton N (2021), Bayesian Hypothesis Testing and Hierarchical Modeling of Ivermectin Effectiveness. $nameOfConference


    • Kyrimi E, McLachlan S, Dube K et al. (2021), A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future $nameOfConference


    • Kyrimi E, Dube K, Fenton N et al. (2021), Bayesian networks in healthcare: What is preventing their adoption? $nameOfConference


    • Fenton N, Gill RD, Lagnado D (2021), Statistical issues in Serial Killer Nurse cases $nameOfConference

    • Dobson R, Fenton N, Hartmann M (2021), Current Review and Next Steps for Artificial intelligence in Multiple Sclerosis risk research $nameOfConference


    • Constantinou AC, Fenton N, Neil M (2021), How do some Bayesian Network machine learned graphs compare to causal knowledge? $nameOfConference

    • Lin P, Neil M, Fenton N (2021), A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models $nameOfConference


    • Hill A, Joyner CH, Keith-Jopp C et al. (2021), A bayesian network decision support tool for low back pain using a RAND appropriateness procedure: proposal and internal pilot study $nameOfConference


    • Constantinou AC, Fenton NE, Neil M (2021), How do some Bayesian Network machine learned graphs compare to causal knowledge? $nameOfConference

    • McLachlan S, Kyrimi E, Daley BJ et al. (2020), Incorporating Clinical Decisions into Standardised Caremaps $nameOfConference


    • McLachlan S, Kyrimi E, Dube K et al. (2020), Lawmaps: Enabling Legal AI development through Visualisation of the Implicit Structure of Legislation and Lawyerly Process $nameOfConference

    • McLachlan S, Paterson H, Dube K et al. (2020), Real-Time Online Probabilistic Medical Computation using Bayesian Networks $nameOfConference


    • Dube K, McLachlan S, Zanamwe N et al. (2020), Managing Knowledge in Computational Models for Global Food, Nutrition and Health Technologies $nameOfConference


    • Osman M, meder B, Fenton N et al. (2020), Learning from behavioural changes that fail $nameOfConference


    • Fenton N, Dewett S, Lagnado D (2020), Propensities and second order uncertainty: a modified taxi cab problem $nameOfConference


    • Hunte J, Neil M, Fenton N (2020), Product risk assessment: a Bayesian network approach $nameOfConference

    • Fenton N, Neil M, Yet B et al. (2020), Analyzing the Simonshaven Case Using Bayesian Networks $nameOfConference


    • Fenton N, Jamieson A, Gomes S et al. (2020), On the limitations of probabilistic claims about the probative value of mixed DNA profile evidence $nameOfConference

    • Fenton N, Neil M, Frazier S (2020), The role of collider bias in understanding statistics on racially biased policing $nameOfConference

    • Fenton N (2020), A Note on UK Covid19 death rates by religion: which groups are most at risk? $nameOfConference

    • Kyrimi E, Neves MR, McLachlan S et al. (2020), Medical idioms for clinical Bayesian network development $nameOfConference

    • Kyrimi E, Raniere Neves M, Mclachlan S et al. (2020), Medical idioms for clinical Bayesian network development $nameOfConference


    • Fenton N, Pilditch T, Ulrike H et al. (2020), Dependencies in evidential reports: The case for informational advantages $nameOfConference


    • Daley BJ, Kyrimi E, Dube K et al. (2020), Data Visualisation in Midwifery: The Challenge of Seeing what Datasets Hide $nameOfConference


    • McLachlan S, Dube K, Hitman GA et al. (2020), Bayesian networks in healthcare: Distribution by medical condition $nameOfConference


    • Dube K, McLachlan S, Zanamwe N et al. (2020), Managing Knowledge in Computational Models for Global Food, Nutrition and Health Technologies $nameOfConference


    • Neil M, Fenton N, Osman M et al. (2020), Bayesian Network Analysis of Covid-19 data reveals higher Infection Prevalence Rates and lower Fatality Rates than widely reported $nameOfConference


    • Neil M, Fenton N, Osman M et al. (2020), Bayesian network analysis of Covid-19 data reveals higher infection prevalence rates and lower fatality rates than widely reported $nameOfConference


    • Fenton N (2020), A note on 'Collider bias undermines our understanding of COVID-19 disease risk and severity' and how causal Bayesian networks both expose and resolve the problem $nameOfConference

    • McLachlan S, Lucas P, Dube K et al. (2020), Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing? $nameOfConference

    • McLachlan S, Kyrimi E, Dube K et al. (2020), Standardising Clinical Caremaps: Model, Method and Graphical Notation for Caremap Specification 2th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5: HEALTHINF


    • Fenton N, Osman M, Mclachlan S et al. (2020), COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing $nameOfConference


    • Fenton N, Neil M, Constantinou A (2020), The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018) $nameOfConference


    • McLachlan S, Kyrimi E, Dube K et al. (2020), Towards standardisation of evidence-based clinical care process specifications $nameOfConference


    • Kyrimi E, McLachlan S, Dube K et al. (2020), A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future $nameOfConference

    • McLachlan S, Dube K, Hitman GA et al. (2020), Bayesian Networks in Healthcare: Distribution by Medical Condition $nameOfConference

    • McLachlan S, Kyrimi E, Fenton N (2020), Public Authorities as Defendants: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden $nameOfConference

    • Lin P, Neil M, Fenton N (2020), Improved high dimensional discrete Bayesian network inference using triplet region construction $nameOfConference


    • Fenton N, Neil M, Constantinou A (2019), Simpson's Paradox and the implications for medical trials $nameOfConference

    • Neil M, Fenton N, Lagnado D et al. (2019), Modelling competing legal arguments using Bayesian model comparison and averaging $nameOfConference


    • Wang J, Neil M, Fenton N (2019), A Bayesian network approach for cybersecurity risk assessment implementing and extending the FAIR model $nameOfConference


    • Mclachlan S, Dube K, KYRIMI E et al. (2019), LAGOS: Learning Health Systems and how they can integrate with patient care $nameOfConference


    • Daley B, Hitman G, Fenton N et al. (2019), Assessment of the methodological quality of local clinical practice guidelines on the identification and management of gestational diabetes. $nameOfConference


    • McLachlan S, Dube K, Johnson O et al. (2019), A framework for analysing learning health systems: Are we removing the most impactful barriers? $nameOfConference


    • Fenton N, Lagnado D, Dahlman C et al. (2019), The Opportunity Prior: A proof-based prior for criminal cases $nameOfConference


    • Neil M, Fenton N, Osman M et al. (2019), Causality, the critical but often ignored component guiding us through a world of uncertainties in risk assessment $nameOfConference


    • FENTON NE, DE ZOETE J, NOGUCHI T et al. (2019), Resolving the so-called “probabilistic paradoxes in legal reasoning” with Bayesian Networks $nameOfConference


    • Neil M, Fenton N, Lagnado D et al. (2019), Modelling Competing Legal Arguments using Bayesian Model Comparison and Averaging $nameOfConference

    • MCLACHLAN S, KYRIMI E, FENTON N et al. (2019), Clinical Caremap Development: How can caremaps standardise care when they are not standardised? HEALTHINF 2019


    • FENTON NE, NEIL M, NOGUCHI T (2019), An extension to the noisy-OR function to resolve the ‘explaining away’ deficiency for practical Bayesian network problem $nameOfConference


    • McLachlan S, Dube K, Gallagher T et al. (2019), Realistic Synthetic Data Generation: The ATEN Framework $nameOfConference


    • Fenton NE, Neil M, Constantinou AC (2019), Simpson's Paradox and the implications for medical trials. $nameOfConference

    • FENTON NE, Pilditch T, Lagnado D et al. (2018), The zero-sum fallacy in evidence evaluation $nameOfConference


    • FENTON NE, NOGUCHI T, NEIL M (2018), Addressing the Practical Limitations of Noisy-OR using Conditional Inter-causal Anti-Correlation with Ranked Nodes $nameOfConference


    • OSMAN M, FENTON NE, Pilditch T et al. (2018), Who do we trust on social policy interventions $nameOfConference


    • Osman M, Fenton N, Pilditch T et al. (2018), Whom Do We Trust on Social Policy Interventions? $nameOfConference


    • Fenton N, Neil M (2018), Improving Software Testing with Causal Modeling $nameOfConference


    • MCLACHLAN S, Potts HWW, Dube K et al. (2018), The Heimdall framework for supporting characterisation of learning health systems $nameOfConference


    • CONSTANTINOU AC, FENTON N (2018), Things to know about Bayesian networks $nameOfConference


    • FENTON NE (2018), Evidence based decision making turns knowledge into power $nameOfConference

    • YET B, NEIL M, FENTON N et al. (2018), An Improved Method for Solving Hybrid Influence Diagrams $nameOfConference


    • FENTON NE, NEIL M (2018), Lawnmowers versus terrorists: A highly misleading view of risk $nameOfConference


    • Yet B, Constantinou A, Fenton N et al. (2018), Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization $nameOfConference


    • FENTON NE, NEIL M (2018), Are lawnmowers a greater risk than terrorists? $nameOfConference

    • McLachlan S, Dube K, Buchanan D et al. (2018), Learning health systems: The research community awareness challenge $nameOfConference


    • FENTON NE, DE ZOETE J, Lagnado D (2017), Modeling complex legal cases as a Bayesian network using idioms and sensitivity analysis with the Collins case as a complete example 10th International Conference on Forensic Inference and Statistics


    • FENTON NE, DE ZOETE J (2017), Automatic Generation of Bayesian networks in Forensic Science 10th International Conference on Forensic Inference and Statistics


    • CONSTANTINOU AC, Fenton N (2017), The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks $nameOfConference


    • FENTON NE, Lagnado D, Dahlman C et al. (2017), The Opportunity Prior: A Simple and Practical Solution to the Prior Probability Problem for Legal Cases International Conference on AI and the Law (ICAIL 17)


    • Neil M, Fenton N (2017), Risk Management Using Bayesian Networks $nameOfConference


    • CONSTANTINOU AC, Fenton NORMAN (2017), Towards Smart-Data: Improving predictive accuracy in long-term football team performance $nameOfConference


    • CONSTANTINOU AC, Fenton N (2017), Improving predictive accuracy using Smart-Data: The case of football teams’ evolving performance $nameOfConference


    • Coid JW, Ullrich S, Kallis C et al. (2016), Improving risk management for violence in mental health services: a multimethods approach $nameOfConference


    • Fenton N, Neil M, Lagnado D et al. (2016), How to model mutually exclusive events based on independent causal pathways in Bayesian network models $nameOfConference


    • DEMENTIEV E, Fenton N (2016), Bayesian Torrent Classification by File Name and Size Only International Conference on Probabilistic Graphical Models

    • Zhou Y, Hospedales TM, Fenton N (2016), When and where to transfer for Bayesian network parameter learning $nameOfConference


    • FENTON NE, Smit N, Lagnado D et al. (2016), Using Bayesian networks to guide the assessment of new evidence in an appeal case $nameOfConference


    • FENTON NE, Zhou Y, Zhu C (2016), An Empirical Study of Bayesian Network Parameter Learning with Monotonic Influence Constraints $nameOfConference


    • Yet B, CONSTANTINOU AC, Fenton N et al. (2016), A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study $nameOfConference


    • CONSTANTINOU AC, FENTON N, NEIL M (2016), Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved $nameOfConference


    • FENTON NE, neil M, Berger D (2016), Bayes and the Law $nameOfConference


    • Constantinou AC, Fenton N, Marsh W et al. (2016), From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support $nameOfConference


    • Constantinou AC, Fenton NE (2016), Improving Predictive Accuracy Using Smart-Data rather than Big-Data: A Case Study of Soccer Teams' Evolving Performance. $nameOfConference

    • Constantinou AC, Yet B, Fenton N et al. (2015), Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences $nameOfConference


    • FENTON NE, Zhou Y, Hospedales T et al. (2015), Probabilistic Graphical Models Parameter Learning with Transferred Prior and Constraints Uncertainty in Artificial Intelligence

    • Shepherd K, Hubbard D, Fenton N et al. (2015), Policy: Development goals should enable decision-making $nameOfConference


    • FENTON NE, Chockler H, Keppens J et al. (2015), Causal Analysis for Attributing Responsibility in Legal Cases International Conference on Artificial Intelligence & Law (ICAIL 15)


    • Chockler H, Fenton N, Keppens J et al. (2015), Causal analysis for attributing responsibility in legal cases $nameOfConference


    • Constantinou AC, Freestone M, Marsh W et al. (2015), Risk assessment and risk management of violent re-offending among prisoners $nameOfConference


    • FENTON NE (2015), Moving from big data and machine learning to smart data and causal modelling: a simple example from consumer research and marketing $nameOfConference


    • de Zoete J, Sjerps M, Lagnado D et al. (2015), Modelling crime linkage with Bayesian networks $nameOfConference


    • Zhou Y, Fenton N, Hospedales TM et al. (2015), Probabilistic graphical models parameter learning with transferred prior and constraints $nameOfConference

    • Fenton N, Bieman J (2014), Software Metrics $nameOfConference


    • Fenton N, Bieman J (2014), Software Metrics A Rigorous and Practical Approach, Third Edition $nameOfConference

    • Lin P, Neil M, Fenton NE (2014), Risk Aggregation in the presence of Discrete Causally Connected Random Variables $nameOfConference


    • Fenton N, Lagnado D, Hsu A et al. (2014), Response to "on the use of the likelihood ratio for forensic evaluation: response to Fenton et al.". $nameOfConference


    • De Zoete J, Sjerps M, Lagnado D et al. (2014), Modelling crime linkage with Bayesian networks $nameOfConference


    • Yet B, Perkins Z, Fenton N et al. (2014), Not just data: a method for improving prediction with knowledge. $nameOfConference


    • Zhou Y, Fenton N, Neil M (2014), An extended MPL-C model for Bayesian network parameter learning with exterior constraints $nameOfConference


    • Fenton N (2014), Assessing evidence and testing appropriate hypotheses $nameOfConference


    • Zhou Y, Fenton N, Neil M (2014), Bayesian network approach to multinomial parameter learning using data and expert judgments $nameOfConference


    • Constantinou AC, Fenton NE, Hunter Pollock LJ (2014), Bayesian networks for unbiased assessment of referee bias in Association Football $nameOfConference


    • Fenton NE, Neil M (2014), Decision Support Software for Probabilistic Risk Assessment Using Bayesian Networks $nameOfConference


    • Yet B, Perkins Z, Fenton N et al. (2014), Not just data: A method for improving prediction with knowledge $nameOfConference


    • Fenton N, Berger D, Lagnado D et al. (2014), When 'neutral' evidence still has probative value (with implications from the Barry George Case) $nameOfConference


    • Fenton NE, Neil M, Hsu A (2013), Calculating and understanding the value of any type of match evidence when there are potential testing errors $nameOfConference


    • Constantinou AC, Fenton NE, Neil M (2013), Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks $nameOfConference


    • Constantinou AC, Fenton NE (2013), Profiting from arbitrage and odds biases of the European football gambling market $nameOfConference


    • FENTON NE, Neil M, Lagnado D (2013), A General Structure for Legal Arguments About Evidence Using Bayesian Networks $nameOfConference


    • Constantinou A, FENTON NE (2013), Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries $nameOfConference


    • Zhou Y, Fenton N, Neil M et al. (2013), Incorporating Expert Judgement into Bayesian Network Machine Learning $nameOfConference

    • Fenton N, Berger D, Lagnado D et al. (2013), When 'neutral' evidence still has probative value (with implications from the Barry George Case) $nameOfConference


    • Constantinou A, FENTON NE, Neil M (2012), pi-football: A Bayesian network model for forecasting Association Football match outcomes $nameOfConference


    • Neil M, Chen X, Fenton NE (2012), Optimizing the Calculation of Conditional Probability Tables in Hybrid Bayesian Networks using Binary Factorization $nameOfConference


    • FENTON NE, Lagnado D, Neil M (2012), Legal idioms: a framework for evidential reasoning $nameOfConference


    • Constantinou A, FENTON NE (2012), Solving the problem of inadequate scoring rules for assessing probabilistic football forecasting models $nameOfConference


    • FENTON NE (2012), A simple story illustrating why pure machine learning (without expert input) may be doomed to fail and totally unnecessary $nameOfConference

    • FENTON NE, Neil M (2012), Risk Assessment with Bayesian Networks $nameOfConference

    • Fenton N, Neil M (2012), Risk assessment and decision analysis with bayesian networks $nameOfConference


    • Fenton N (2011), Science and law: Improve statistics in court. $nameOfConference


    • FENTON N (2011), Rational software developers as pathological code hackers $nameOfConference


    • FENTON NE, Neil M (2011), Avoiding Legal Fallacies in Practice Using Bayesian Networks $nameOfConference

    • FENTON NE, Neil M (2011), Risk Assessment with Bayesian Networks $nameOfConference

    • FENTON NE, Neil M (2011), The use of Bayes' and causal modelling in decision making, uncertainty and risk $nameOfConference

    • Fenton N, Neil M (2010), Comparing risks of alternative medical diagnosis using Bayesian arguments. $nameOfConference


    • Marquez D, Neil M, Fenton N (2010), Improved reliability modeling using Bayesian networks and dynamic discretization $nameOfConference


    • Xiangjun L, Fenton NE (2010), Extending support vector machines to discover temporal periodic patterns. Second Global Congress on Intelligent Systems (GCIS 2010)


    • Neil M, Marquez D, Fenton N (2010), Improved Reliability Modeling using Bayesian Networks and Dynamic Discretization $nameOfConference


    • Fenton NE, Hearty P, Neil M et al. (2010), Software project and quality modelling using Bayesian networks $nameOfConference


    • Fineman M, Radlinski L, Fenton NE (2009), Modelling project trade-off using Bayesian networks IEEE Int. Conf. Computational Intelligence and Software Engineering


    • Hearty P, Fenton N, Marquez D et al. (2009), Predicting Project Velocity in XP Using a Learning Dynamic Bayesian Network Model $nameOfConference


    • Fineman M, Fenton NE (2009), Quantifying risks using Bayesian networks IASTED Int. Conf. Advances in Management Science and Risk Assessment (AMSRA 2009)

    • N Fenton MN, Radliński Ł (2009), Software Project and Quality Modelling Using Bayesian Networks $nameOfConference


    • Marquez D, Neil M, Fenton N (2008), Solving dynamic fault trees using a new hybrid Bayesian network inference algorithm $nameOfConference


    • Fenton N, Neil M, Marsh W et al. (2008), On the effectiveness of early life cycle defect prediction with Bayesian Nets $nameOfConference


    • Neil M, Tailor M, Marquez D et al. (2008), Modelling dependable systems using hybrid Bayesian networks $nameOfConference


    • Fenton NE, Neil M (2008), Avoiding legal fallacies in practice using Bayesian networks Seventh International Conference on Forensic Inference and Statistics

    • Neil M, Tailor M, Marquez D et al. (2008), Modelling dependable systems using hybrid Bayesian networks $nameOfConference


    • Marquez D, Neil M, Fenton N (2008), Solving Dynamic Fault Trees using a New Hybrid Bayesian Network Inference Algorithm $nameOfConference


    • Neil M, Marquez D, Fenton N (2008), Using Bayesian Networks to Model the Operational Risk to Information Technology Infrastructure in Financial Institutions $nameOfConference

    • Fenton NE, Neil M, Marquez D (2008), Using Bayesian Networks to Predict Software Defects and Reliability $nameOfConference

    • Fenton NE, Neil M, Caballero JG (2007), Using ranked nodes to model qualitative judgments in Bayesian Networks $nameOfConference


    • Radliński Ł, Fenton NE, Marquez D et al. (2007), Empirical Analysis of Software Defect Types $nameOfConference

    • Hall T, Fenton N (2007), Implementing effective software metrics programs $nameOfConference


    • Radliński Ł, Fenton NE, Neil M et al. (2007), Improved Decision-Making for Software Managers Using Bayesian Networks $nameOfConference

    • Fenton NE (2007), Making Sense of Probability: Fallacies, Myths and Puzzles $nameOfConference

    • Fenton NE, Neil M (2007), Managing Risk in the Modern World: Bayesian Networks and the Applications $nameOfConference

    • Radliński Ł, Fenton NE, Neil M et al. (2007), Modelling Prior Productivity and Defect Rates in a Causal Model for Software Project Risk Assessment $nameOfConference

    • Fenton N, Neil M, Marsh W et al. (2007), Predicting software defects in varying development lifecycles using Bayesian nets $nameOfConference


    • Fenton NE, Neil M, Marsh W et al. (2007), Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction ICSE PROMISE (Predictive Models in Software Engineering) 07


    • Khodakarami V, Fenton N, Neil M (2007), Project Scheduling: Improved approach to incorporate uncertainty using Bayesian Networks $nameOfConference


    • Neil M, Fenton N, MARSH DWR (2006), A Software Metrics Challenge: Data for Project Prediction 29th International Conference on Software Engineering (ICSE 2007), Minneapolis, USA

    • Fenton N, Neil M (2006), Comment: Expert elicitation for reliable system design $nameOfConference


    • Fenton N, Neil M (2006), Expert elicitation for reliable system design - Comment $nameOfConference


    • Joseph A, Fenton NE, Neil M (2006), Predicting football results using Bayesian nets and other machine learning techniques $nameOfConference


    • Fenton N, Wang W (2006), Risk and confidence analysis for fuzzy multicriteria decision making $nameOfConference


    • FENTON NE (2006), Report of Norman Fenton for the Technology and Construction Court, Reference HT 05 181 $nameOfConference

    • Neil M, FENTON NE (2006), AgenaRisk $nameOfConference

    • Fenton N (2006), Book Review: An Alternative Internet: Radical Media, Politics and Creativity $nameOfConference


    • Neil M, FENTON NE, Krause P et al. (2006), Bayesian networks for software process control $nameOfConference

    • Neil M, FENTON NE, Radlinski L (2006), Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation $nameOfConference


    • Neil M, Tailor M, Fenton N et al. (2006), Modeling Dependable Systems using Hybrid Bayesian Networks $nameOfConference


    • Neill M, Tailor M, Marquez D et al. (2006), Modeling dependable systems using hybrid Bayesian networks $nameOfConference


    • Neil M, FENTON NE, Marsh W et al. (2006), Predicting Software Defects in Varying Development Lifecycles using Bayesian Nets ICSE (International Conference on Software Engineering) 2006, May 20-28, 2006, Shanghai, China

    • Neil M, Fenton N, Tailor M (2005), Using Bayesian networks to model expected and unexpected operational losses $nameOfConference


    • Fenton N (2005), Book Review: Contesting Media Power: Alternative Media in a Networked World $nameOfConference


    • Fenton NE, Neil M (2005), A Critique of Software Defect Prediction Models $nameOfConference

    • Hearty P, FENTON NE, Neil M et al. (2005), Automated population of causal models for improved software risk assessment 20th IEEE/ACM International Conference on Automated Software Engineering


    • Fenton N (2005), Baltimore's water supply 1787-1854 - Meeting the needs of a growing city $nameOfConference

    • NEIL MD, Fenton NE (2005), Improved Methods for building large-scale Bayesian Networks Uncertainty in Artificial Intelligence (UAI) 2005, Edinburgh University

    • NEIL MD, Fenton NE (2005), Improved Software Defect Prediction 10th European SEPG, London

    • Fenton N, Marsh W, Neil M et al. (2004), Making resource decisions for software projects $nameOfConference


    • Fenton N (2003), Reviews $nameOfConference


    • Neil M, FENTON NE (2003), Improved Programme Selection $nameOfConference

    • NEIL M, Fenton NE (2003), Improved programme selection. $nameOfConference

    • Neil M, Fenton N, Forey S et al. (2003), Assessing vehicle reliability using Bayesian networks $nameOfConference

    • Neil M, FENTON NE, Krause P (2003), Software Quality Prediction Using Bayesian Networks $nameOfConference


    • Fenton N, Krause P, Neil M (2002), Software measurement: Uncertainty and causal modeling $nameOfConference


    • Fenton N (2002), Bayesian Belief Networks (BBNs) $nameOfConference


    • Fenton NE, Krause P, Neil M (2002), Probabilistic Modelling for Software Quality Control $nameOfConference


    • Fenton N, Neil M (2001), Making decisions: using Bayesian nets and MCDA $nameOfConference


    • Fenton N (2001), Viewpoint article: Conducting and presenting empirical software engineering $nameOfConference


    • Norman Fenton, Paul Krause, Martin Neil et al. (2001), A Probabilistic Model for Software Defect Prediction $nameOfConference

    • Neil M, Fenton N, Forey S et al. (2001), Using Bayesian belief networks to predict the reliability of military vehicles $nameOfConference


    • FENTON NE (2001), Conducting and Presenting Empirical Software Engineering $nameOfConference

    • Bergman L, Fenton N, Verdún JDC et al. (2001), Perspectives $nameOfConference


    • Fenton N, Krause P, Neil M (2001), Probabilistic modelling for software quality control $nameOfConference


    • Neil M, FENTON NE, Krause P (2001), Software Metrics: Uncertainty and causal Modelling EuroSPI conference, Limerick Institute of Technology


    • Fenton N (2000), The problematics of postmodernism for feminist media studies $nameOfConference


    • Neil M, Fenton N, Nielsen L (2000), Building large-scale Bayesian networks $nameOfConference


    • Fenton NE (2000), Quantitative analysis of faults and failures in a complex software system $nameOfConference


    • Norman Fenton, Martin Neil (2000), The "Jury Observation Fallacy" and the use of Bayesian Networks to present Probabilistic Legal Arguments $nameOfConference

    • Fenton NE, Neil M (2000), Software metrics: roadmap ICSE 2000: Proceedings of the Conference on The Future of Software Engineering


    • Fenton NE, Neil M (2000), The Jury Fallacy and the use of Bayesian nets to simplify probabilistic legal arguments $nameOfConference

    • Norman Fenton, Martin Neil (1999), Making Decisions: Bayesian Nets and MCDA $nameOfConference

    • Norman Fenton, Martin Neil (1999), Software Metrics and Risk $nameOfConference

    • Fenton NE, Neil M (1999), A critique of software defect prediction models $nameOfConference


    • Finney K, Fenton N, Fedorec A (1999), Effects of structure on the comprehensibility of formal specifications $nameOfConference


    • Deacon D, Fenton N, Bryman A (1999), From inception to reception: the natural history of a news item $nameOfConference


    • Fenton NE, Neil M (1999), Software metrics: successes, failures and new directions $nameOfConference


    • Fenton N (1998), Review Essay: Media Youth and Technological Futures $nameOfConference


    • Norman E Fenton, Niclas Ohlsson (1998), Quantitative Analysis of Faults and Failures in a Complex Software System $nameOfConference

    • Fenton NE, Neil M (1998), A strategy for improving safety related software engineering standards $nameOfConference


    • Fenton NE, Littlewood B, Neil M et al. (1998), Assessing dependability of safety critical systems using diverse evidence $nameOfConference

    • Fenton NE (1998), Why most software quality metrics do not measure software quality 2nd Annual SQI Sympposium

    • Fenton N (1997), Reviews $nameOfConference


    • Morasca S, Briand LC, Basili VR et al. (1997), Comments on "Towards a framework for software measurement validation" $nameOfConference


    • Fenton N, Pfleeger SL (1997), Can formal methods always deliver? $nameOfConference

    • Fenton N, Bryman A, Deacon D et al. (1997), ‘Sod off and Find Us a Boffin’: Journalists and the Social Science Conference $nameOfConference


    • Ohlsson N, Fenton NE (1997), Experience with data collection in a large scale environment Workshop on Empirical Studies of Software Maintenance (WESS 97)

    • Fenton NE (1997), How to improve safety-critical standards $nameOfConference


    • Hall T, Fenton NE (1997), Implementing effective software metrics programs $nameOfConference


    • Ohlsson N, Fenton NE (1997), Let's start testing some basic software hypotheses! $nameOfConference

    • Kitchenham B, Pfleeger SL, Fenton N (1997), Reply To: Comments On "towards A Framework Of Software Measurement Validation" $nameOfConference


    • Fenton NE, Pfleeger SL (1997), Software Metrics: A Rigorous and Practical Approach (2nd Edition) $nameOfConference

    • Hall T, Fenton NE (1996), Software quality programmes: a snapshot of theory versus reality $nameOfConference


    • Neil M, Littlewood B, Fenton NE (1996), Applying Bayesian belief networks to systems dependability assessment $nameOfConference


    • Fenton NE (1996), Do standards improve product quality? $nameOfConference


    • Finney K, Fenton NE (1996), Evaluating the effectiveness of using Z: the claims made about CICS and where we go from here $nameOfConference


    • Bieman JM, Fenton NE, Gustafson DA et al. (1996), Fundamental issues in software measurement $nameOfConference

    • Fenton NE, Melton A (1996), Measurement theory and software measurement $nameOfConference

    • Schneidewind NF, Fenton N (1996), Point counterpoint: do standards improve quality? $nameOfConference


    • Strigini L, Fenton NE (1996), Rigorously assessing software reliability and safety $nameOfConference

    • Fenton NE (1996), The empirical basis for software engineering $nameOfConference

    • Fenton NE (1996), The role of measurement in software safety assessment $nameOfConference


    • Fenton N (1995), Women, Communication and Theory: A Glimpse of Feminist Approaches to Media and Communication Studies $nameOfConference


    • Fenton NE (1995), Software Measurement: A Necessary Scientific Basis $nameOfConference


    • Littlewood B, Brocklehurst S, Fenton N et al. (1995), Towards Operational Measures of Computer Security: Concepts $nameOfConference


    • Kitchenham B, Pfleeger SL, Fenton N (1995), Towards a framework for software measurement validation $nameOfConference


    • EBERT C, FENTON N (1994), CONTROVERSY REVISITED $nameOfConference

    • FENTON N, PFLEEGER SL, GLASS B (1994), WHATS WRONG WITH INCREMENTAL DEVELOPMENT - REPLY $nameOfConference

    • Pfleeger SL, Fenton N, Page S (1994), Evaluating software engineering standards $nameOfConference


    • Hall T, Fenton NE (1994), Implementing software metrics - the critical success factors $nameOfConference


    • Fenton NE, Pfleeger SL, Glass RL (1994), Science and substance: a challenge to software engineers $nameOfConference


    • Fenton NE (1994), Software Metrics: a Practitioner's Guide to Improved Product Development $nameOfConference


    • Fenton N (1994), Software engineering metrics. Vol. 1: Measures and validations. Martin Shepperd, Published by McGraw‐Hill Book Company Europe, Maidenhead, U.K., 1993. ISBN 0‐07‐707410‐6, 302 pages. Price: £35.00, hard cover $nameOfConference


    • Fenton NE (1994), Software measurement: a necessary scientific basis $nameOfConference


    • Devine C, Fenton NE, Page S (1993), Deficiencies in existing software engineering standards as exposed by SMARTIE $nameOfConference

    • Fenton NE, Littlewood B, Page S (1993), Evaluating software engineering standards and methods $nameOfConference

    • Fenton NE (1993), How effective are software engineering methods? $nameOfConference


    • Fenton N (1993), Position paper $nameOfConference


    • Fenton N (1993), Session 2 summary: Objectives and context of measurement/experimentation $nameOfConference


    • Fenton NE, Page S, Devine C (1993), Software engineering standards: evaluation and improvements $nameOfConference

    • Fenton NE, Hill G (1993), Systems Construction and Analysis: A Mathematical and Logical Approach $nameOfConference

    • Fenton NE (1993), The effectiveness of software engineering methods $nameOfConference

    • Littlewood B, Brocklehurst S, Fenton NE et al. (1993), Towards operational measures of security $nameOfConference


    • Fenton NE, Page S (1993), Towards the evaluation of software engineering standards $nameOfConference


    • Bieman J, Fenton NE, Gustafson D et al. (1992), Moving from philosophy to practice in software measurement $nameOfConference


    • Fenton NE (1992), Software measurement: why a formal approach? $nameOfConference


    • Fenton N (1992), Software quality: Theory and management. Alan C. Gillies. Published by Chapman & Hall, London, U.K., 1992. ISBN 0 412 4513 0, 250 pages. Price: £19.95, Soft Cover $nameOfConference


    • Fenton NE (1992), When a software measure is not a measure $nameOfConference


    • FENTON NE, WHITTY RW (1991), PROGRAM STRUCTURES - SOME NEW CHARACTERIZATIONS $nameOfConference


    • Fenton N (1991), Software complexity. Measures and methods: Horst Zuse 605 pages, ISBN 3‐11‐012226‐X; DM 158, US $99.95; Published by Walter de Gruyter, Berlin. New York $nameOfConference


    • Fenton NE, Whitty RW (1991), Program structures: some new characterizations $nameOfConference


    • Fenton NE (1991), Software Metrics: A Rigorous Approach $nameOfConference

    • Fenton NE, Littlewood B (1991), Software Reliability and Metrics $nameOfConference

    • Fenton NE (1991), The mathematics of complexity in computing and software engineering $nameOfConference

    • Fenton NE, Kitchenham BA (1991), Validating software measures $nameOfConference


    • FENTON N (1990), STREET CHILDREN $nameOfConference

    • Baker AL, Bieman JM, Fenton NE et al. (1990), A philosophy for software measurement $nameOfConference


    • Fenton NE, Melton A (1990), Deriving structurally based software measures $nameOfConference


    • Bush M, Fenton NE (1990), Software measurement: a conceptual framework $nameOfConference


    • Fenton NE (1990), Software metrics: theory, tools and validation $nameOfConference


    • Corner J, Richardson K, Fenton N (1990), Textualizing risk: TV discourse and the issue of nuclear energy $nameOfConference


    • Fenton NE (1989), Software measurement and analysis: a case study in collaborative research $nameOfConference

    • Bache R, Fenton NE, Tinker R et al. (1988), Software quality assurance, a rigorous engineering practice $nameOfConference

    • Fenton NE, Mole D (1988), A note on the use of Z for flowgraph decomposition $nameOfConference


    • Fenton NE, Kaposi AA (1987), Metrics and software structure $nameOfConference


    • FENTON NE, WHITTY RW (1986), AXIOMATIC APPROACH TO SOFTWARE METRICATION THROUGH PROGRAM DECOMPOSITION $nameOfConference


    • Fenton NE, Whitty RW (1986), Axiomatic approach to software metrication through program decomposition $nameOfConference


    • Fenton N (1985), Network service for data exchange $nameOfConference


    • FENTON NE, WHITTY RW, KAPOSI AA (1985), A GENERALIZED MATHEMATICAL-THEORY OF STRUCTURED PROGRAMMING $nameOfConference


    • WHITTY RW, FENTON NE, KAPOSI AA (1985), A RIGOROUS APPROACH TO STRUCTURAL-ANALYSIS AND METRICATION OF SOFTWARE $nameOfConference


    • Fenton NE, Whitty RW, Kaposi AA (1985), A generalised mathematical theory of structured programming $nameOfConference


    • Whitty RW, Fenton NE, Kaposi AA (1985), A rigorous approach to structural analysis and metrication of software $nameOfConference


    • Whitty RW, Fenton NE (1985), An axiomatic approach to systems complexity $nameOfConference

    • Whitty RW, Fenton NE, Kaposi AA (1985), Structured programming: a tutorial guide $nameOfConference


    • Fenton NE (1985), The structural complexity of flowgraphs Graph Theory and its applications to Algorithms and Computer Science

    • Fenton N (1984), Standards for electronic data exchange $nameOfConference


    • Fenton NE (1984), Matroid Representation of Projective Spaces $nameOfConference


    • Fenton NE (1984), Matroid Representations: an algebraic treatment $nameOfConference


    • Fenton NE (1984), Representations of projective geometries $nameOfConference

    • Fenton NE (1983), Characterisation of Atomic Matroids $nameOfConference


    • Fenton NE, Vamos P (1982), Matroid interpretation of maximal k-arcs in projective spaces $nameOfConference

    • FENTON N (1981), Representation of Matroids Sheffield University, Dept of Mathematics

    • Fenton NE (1981), Representation of Matroids (PhD Thesis) $nameOfConference

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