The Physics of Everyday life

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The combination of Bayesian statistics and neural networks has proven to excel in predictive analytics. Blue Yonders solution NeuroBayes was developed and applied first in the field of particle physics but it can be successfully applied to a broad range of everyday problems for example demand prediction in retail. In this talk we introduce the basic concepts and explain the structure, components, and operations that build up an application for prediction.

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The Physics of Everyday life

  1. 1. The Physics of Everyday Life Bayesian Neural Networks in Business Applications Big Data User Group – July 2013 Meetup in Karlsruhe Dr. F. Wick, Blue Yonder GmbH & Co. KG felix.wick@blue-yonder.com
  2. 2. Our Background: High Energy Physics Fundamental research at the forefront of science A few key questions in High Energy Physics: » Our current theoretical understanding is called the “Standard Model” » Extremely well tested, some of its aspects have the most precise agreement between theoretical predictions and experimental results across all sciences » What happened at the beginning of the universe? » Does our theory remain valid under such extreme conditions or is it a “low energy approximation” of a more fundamental theory? » What is the origin of mass? A Higgs boson has been found » Is it the Higgs boson? » Why is there so much matter left in the universe? » All matter should have annihilated with anti-matter, where does this asymmetry come from? Photo: CERN DI-2-8-91 The Physics of Everyday LifeJuly 20132
  3. 3. The Large Hadron Collider at CERN Built to understand how exactly our universe works Schreiben Sie hier Ihren Text LHC: 27km circumference Photo: CERN The Physics of Everyday LifeJuly 20133
  4. 4. Big Data in Particle Physics At the LHC (CERN) - per experiment: • 40 000 000 events per second • up to 1 PetaByte per second raw data • 1 PB of data get stored per year searching for the needle in the hay stack… Need to filter out the „interesting“ events in real-time Photo: CERN The Physics of Everyday LifeJuly 20134
  5. 5. Artificial Neural Networks and NeuroBayes The Physics of Everyday Life ► NeuroBayes classification core based on simple feed forward neural network ► Information coded in connections between neurons ► Each neuron performs fuzzy decisions ► Neural networks learn from examples ► Human brain: about 1011 neurons about 1014 connections ► NeuroBayes: 10 to few 100 neurons July 20135
  6. 6. Bayes‘ Theorem and NeuroBayes The Physics of Everyday Life Posterior Evidence Likelihood Prior ► NeuroBayes internally uses Bayesian arguments for regularisation ► NeuroBayes automatically makes Bayesian posterior statements July 20136
  7. 7. A little more Detail The Physics of Everyday LifeJuly 20137
  8. 8. Mode I: Classification Issues The Physics of Everyday Life Classification: Binary targets: Each single outcome will be “yes“ or “no“ NeuroBayes output is the Bayesian posterior probability that answer is “yes“ (given that inclusive rates are the same in training and test sample, otherwise simple transformation necessary). Examples: ► This elementary particle is a muon. ► Customer Meier will cancel his contract next year. July 20138
  9. 9. Probability density for real valued targets: For each possible (real) value a probability (density) is given. From that all statistical quantities like mean value, median, mode, standard deviation, etc. can be deduced. Mode II: Regression Issues The Physics of Everyday Life Examples: ► Energy of an elementary particle ► Turnaround of an article next year July 20139
  10. 10. Historic or simulated data Data set a = ... b = ... c = ... .... t = …! NeuroBayes® Teacher NeuroBayes® Expert New or real data Data set a = ... b = ... c = ... .... t = ? Expertise Expert system f t Probability that hypothesis is correct (classification) or probability density for variable t t How it works: Training and Prediction The Physics of Everyday LifeJuly 201310
  11. 11. What does the future hold? What would happen if... ... a large supermarket chain knew precisely how much fresh fruit it will sell? The Physics of Everyday LifeJuly 201311
  12. 12. Blue Yonder – forward looking, forward thinking Now about 100 employees of which most are post-docs, mainly from HEP. Doubling our numbers in 2012, 2013 is looking good… 3 Offices: Karlsruhe, Hamburg (Germany) London (UK) Started as a spin-off from the University of Karlsruhe, Germany supported by the Federal Ministry for Education and Research. The Physics of Everyday LifeJuly 201312
  13. 13. Use all available and relevant information as input, e.g. measurements from the various sub-detectors, … NeuroBayes will extract statistically significant patterns in the data to derive the prediction. Prediction will return the best estimator for a measurement including a statistically sound estimation of the expected spread. 100Energy Momentum Direction Type 50 90 Sub-Detector Distance 200 Calo Kaon ... propabilityP Particle Property E(X) NeuroBayes from Science to Industry Predictive Analytics in High Energy Physics The Physics of Everyday LifeJuly 201313
  14. 14. Use all available and relevant information as input, e.g. article properties, previous sales, etc NeuroBayes will extract statistically significant patterns in the data to derive the prediction. Prediction will return e.g. the most probable sales rate including a statistically sound estimation of the expected spread. Article size Picture size colour Previous sales M 21% red brand price 19,9 171 24 ... propabilityP Prediction sales E(X) NeuroBayes from Science to Industry Predictive Analytics in industry E.g. Retail NeuroBayes allows data-driven analysis and forecasts – both in science and industry The Physics of Everyday LifeJuly 201314
  15. 15. Automated replenishment in supermarkets Fondsmanagement Insurance: Risk prediction Fashion: Sales prediction Media Churn Management Artikelabsatz P E(X)=413 Stock Exchange Order Placement System SAP NeuroBayes® NeuroBayes from Science to Industry Predictive Analytics is the key to many industries The Physics of Everyday LifeJuly 201315
  16. 16. » Most conventional methods: Forecast is a single number » No estimate how precise this number is » Does not allow to handle asymmetric distribution of probabilities » NeuroBayes: Prediction of a full probability density distribution Asymmetric probability density distribution X1: most probable value (n.b. all other values may still occur) P (x) quantity(x) x1 Optimal estimate for your use-case X2: Median: 50% of all values are smaller, 50% larger than this x2 Get more from the Forecasts The Physics of Everyday LifeJuly 201316
  17. 17. Conclusion » Exploiting “Big data” is the next “big” challenge to advance industry » “In this war for customers, the ammunition is data — and lots of it […]” (G. Hawkings, Harvard Business Review, Sep. 2012) » This is the day and age of Predictive Analytics » Data-driven business instead of models and assumptions » Peta-bytes of data and machine learning techniques allow statistically sound analyses » Blue Yonder: From “Big Science” to “Big Business” » Background in High Energy Physics: Crossing the bridge from understanding the behaviour of the fundamental particles at the origin of the universe to the “Big Bang” in sales forecast, risk analysis, churn management, etc. » Versatile NeuroBayes machine learning solution allows to optimise a wide range of business cases The Physics of Everyday LifeJuly 201317
  18. 18. Thank you very much For your attention!
  19. 19. Disclaimer This Presentation (the Presentation) has been prepared by Blue Yonder GmbH & Co KG (collectively, with any officer, director, employee, advisor or agent of any of them, the Preparers) for the purpose of setting out certain confidential information in respect of Blue Yonder’s business activities and strategy. References to the “Presentation” includes any information which has been or may be supplied in writing or orally in connection with the Presentation or in connection with any further inquiries in respect of the Presentation. This Presentation is for the exclusive use of the recipients to whom it is addressed. This Presentation and the information contained herein is confidential. 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