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Slide 1 Bayesian Seminar 16 October 2015 Norman Fenton Queen Mary University of London and Agena Ltd Bayesian Networks why smart data is better than big data Slide 2 Slide 2 Outline From Bayes to Bayesian networks Why pure machine learning is insufficient Applications Way forward Slide 3 Slide 3 FROM BAYES TO BAYESIAN NETWORKS Slide 4 Slide 4 Introducing Bayes E (Positive Test?) We get some evidence E H (Person has disease?) We have a hypothesis H 1 in a 1000 100% accurate for those with disease; 95% accurate for those without What is the probability a person has the disease if they test positive? Slide 5 Slide 5 Bayes Theorem We know the (likelihood) values for P(E|H) We have a prior P(H) = 0.001 P(H|E) = P(E|H)*P(H) P(E) P(E|H)*P(H) P(E|H)*P(H) + P(E|not H)*P(not H) = But we want the posterior P(H|E) Waste of time showing this to most people!!! 1*0.001 1*0.001 + 0.05*0.999 P(H|E) 2% = = 0.001 0.5005 0.0196 = Slide 6 Slide 6 Imagine 1,000 people Slide 7 Slide 7 One has the disease Slide 8 Slide 8 But about 5% of the remaining 999 people without the disease test positive. That is about 50 people Slide 9 Slide 9 So about 1 out of 50 who test positive actually have the disease Thats about 2% Thats very different from the 95% assumed by most medics Slide 10 Slide 10 A more realistic scenario Disease Y Disease Z Test B Disease X Test A Cause 1Cause 2 Symptom 1 Symptom 2 The necessary Bayesian propagation calculations quickly become extremely complex This is a Bayesian network Slide 11 Slide 11 The usual big mistake Combined Evidence/data Combined Hypothesis Slide 12 Slide 12 The Barry George case Slide 13 Slide 13 The Barry George case George fired gun Evidence George fired gun Slide 14 Slide 14 Late 1980s breakthrough Pearl Lauritzen and Spiegelhalter Slide 15 Slide 15 A Classic BN Slide 16 Slide 16 Marginals Slide 17 Slide 17 Dyspnoea observed Slide 18 Slide 18 Also non- smoker Slide 19 Slide 19 Positive x-ray Slide 20 Slide 20..but recent visit to Asia Slide 21 Slide 21 How to develop complex models Can we really LEARN this kind of model from data? Slide 22 Slide 22 How to develop complex models Idioms Cause consequence idiom Measurement idiom Induction idiom Definitional idiom Slide 23 Slide 23 How to develop complex models Bayesian net objects Slide 24 Slide 24 How to develop complex models Ranked nodes Slide 25 Slide 25 Static discretisation: marginals Slide 26 Slide 26 Dynamic discretisation: marginals Slide 27 Slide 27 Static discretisation with observations Slide 28 Slide 28 Dynamic discretisation with observations Slide 29 Slide 29 WHY PURE MACHINE LEARNING IS INSUFFICIENT Slide 30 Slide 30 A typical data-driven study AgeDelay in arrival Injury type Brain scan result Arterial pressure Pupil dilation Outcome (death y/n) 1725ANLYN 3920BNMYN 2365ANLNY 2180CYHYN 6820BYMYN 2230ANMNY .. Slide 31 Slide 31 Delay in arrival Brain scan result Arterial pressure Pupil dilation Age Outcome BN Model learnt purely from data Injury type Slide 32 Slide 32 Outcome Regression model learnt purely from data Delay in arrival Brain scan result Arterial pressure Pupil dilation Age Injury type Slide 33 Slide 33 Delay in arrival Injury type Expert causal BN with hidden explanatory and intervention variables Brain scan result Arterial pressure Pupil dilation Seriousness of injury Outcome Treatment Age Ability to recover Slide 34 Slide 34 CustomerAge Marital status Employment status Home owner SalaryLoanDefaulted 137MEmployedY5000010000N 245MSelf-employedY600005000N 326MSelf-employedY3000020000Y 429SEmployedN5000015000N 526MEmployedY9000020000N 635SSelf-employedN7000020000Y 732MSelf-employedY400005000N 837MEmployedY25000 Y 918SUnemployedN050000N 1040MEmployedY6500045000N 1121SEmployedN2000010000Y 1230SEmployedN400005000N 1322MSelf-employedN3000010000Y 1435MUnemployedY03000Y 1519SUnemployedN0100000N 10000134MEmployedY450001000N 10000228SSelf-employedN250002000N 10000319SUnemployedN025000N Danger of pure data driven decision making: Example of a Bank database on loans Slide 35 Slide 35 Other examples Massive databases cannot learn even tiny models The massive shadow cast by Simpsons paradox See:www.probabilityandlawblogspot.co.uk Slide 36 Slide 36 APPLICATIONS Slide 37 Slide 37 Legal arguments and forensics Slide 38 Slide 38 Football prediction overview Slide 39 Slide 39 Parameter learning from past data Slide 40 Slide 40 Game specific information Slide 41 Slide 41 Taking account of fatigue Slide 42 Slide 42 Incorporating recent match data Slide 43 Slide 43 Final prediction Slide 44 Slide 44 Final prediction www.pi-football.com Constantinou, A., N. E. Fenton and M. Neil (2013): "Profiting from an Inefficient Association Football Gambling Market: Prediction, Risk and Uncertainty Using Bayesian Networks". Knowledge-Based Systems. Vol 50, 60-86 Slide 45 Slide 45 Trauma Care Case Study QM RIM Group The Royal London Hospital US Army Institute of Surgical Research Slide 46 Slide 46 Improving on MESS Score method Slide 47 Slide 47 Life Saving: Prediction of Physiological Disorders Slide 48 Slide 48 Limb Saving: Prediction of Limb Viability Slide 49 Slide 49 www.traumamodels.com Slide 50 Slide 50 Slide 51 Slide 51 Slide 52 Slide 52 Operational Risk Slide 53 Slide 53 WAY FORWARD Slide 54 Slide 54 Big Data Smart Data Knowledge machine learning Big Data or Smart Data? causal models Slide 55 Slide 55 Challenges Building good models with minimal data Tackle resistance to subjective priors Make BN models easier to use and understand BAYES-KNOWLEDGE bayes-knowledge.com Slide 56 Slide 56 Conclusions Bayesian calculations can and should be done with BN tools Some of the most serious limitations of BN tools and algorithms have been resolved BNs have been used effectively in a range of real world problems. Most of these BNs involve expert judgment and not just data Slide 57 Slide 57 Follow up Try the free software and models AgenaRisk.com Get the book BayesianRisk.com Propose case study for BAYES-KNOWLEDGE bayes-knowledge.com Get the papers eecs.qmul.ac.uk/~norman