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.

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. 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
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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
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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
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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
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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
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7. A little more Detail
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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.
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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
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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
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11. What does the future hold?
What would
happen if...
... a large supermarket chain knew precisely how much fresh fruit it will sell?
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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.
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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
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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
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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
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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
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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. Thank you very much
For your attention!

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