Bayesian Networks Phd Thesis

Bayesian Networks Phd Thesis-76
Having recently attained her Ph D in computer science at Queen Mary University of London.Her thesis is titled “Bayesian Networks for Clinical Decision Making: Support, Assurance, Trust”.

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At Queen Mary he teaches decision and risk analysis and software engineering.

Evangelia is a statistician who works as a research assistant on the Pambayesian project.

Scott is currently working on the PAMBAYESIAN project as a postdoctoral research assistant.

He accumulated over 17 years experience in Infrastructure and Software Architecture, systems integration and Workflow design before returning to university to complete graduate and postgraduate qualifications and research in Health Informatics, Health and Cyber Law, Knowledge Engineering and Data Mining.

The new third edition of his book “Software Metrics: A Rigorous and Practical Approach” was published in November 2014.

William’s research aims are to develop better ways to build useful risk and decision making techniques, using a combination of data and knowledge (or expertise).Bridget has been engaged as the Midwifery Research Fellow on the Gestational Diabetes Mellitus (GDM) Pam Bayesian Case Study.Dylan has over 25 years experience working in sports and exercise medicine.Since 1995 he has been Professor of Molecular Medicine and Diabetes at Barts and The London School of Medicine and Dentistry.Prof Hitman was director of the Blizard Institute from October 2013 to March 2017 Martin is a Professor in Computer Science and Statistics in Queen Mary, University of London.His research interests cover Bayesian modeling and risk quantification in diverse areas.Experience in applying Bayesian methods to real problems has convinced him that intelligent risk assessment and decision analysis requires knowledge and data. He is also a joint founder and of Agena Ltd, who develop and distribute Agena Risk, a software product for modeling risk and uncertainty .Norman’s current research focuses primarily on quantitative risk assessment.This typically involves analysing and predicting the probabilities of unknown events using Bayesian statistical methods including especially causal, probabilistic models (Bayesian Networks).This type of reasoning enables improved assessment by taking account of both statistical data and also expert judgment.In addition, Norman has a long track record of work in software engineering (including pioneering work on software metrics).


Comments Bayesian Networks Phd Thesis


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