Bayesian Networks - Intro - Wolfram Burgard, Luc De Raedt, Kristian Kersting, Bernhard Nebel Albert-Ludwigs University Freiburg, Germany PCWP CO HRBP HREKG HRSAT ERRCAUTER HR HISTORY CATECHOL SAO2 EXPCO2 ARTCO2 VENTALV VENTLUNG VENITUBE DISCONNECT MINVOLSET VENTMACH KINKEDTUBE INTUBATION PULMEMBOLUS PAP SHUNT ANAPHYLAXIS MINOVL PVSAT FIO2 PRESS INSUFFANESTH TPR LVFAILURE ERRBLOWOUTPUT STROEVOLUME LVEDVOLUME HYPOVOLEMIA CVP BP Mainly based on F. V. Jensen, „Bayesian Networks and Decision Graphs“, Springer-Verlag New York, 2001. Advanced I WS 06/07 Bayesian Networks Bayesian Networks Advanced I WS 06/07 Why bother with uncertainty? Uncertainty appears in many tasks – Partial knowledge of the state of the world – Noisy observations – Phenomena that are not covered by our models – Inherent stochasticity - Introduction - Introduction Bayesian Networks Bayesian Networks Advanced I WS 06/07 Recommendation Systems Real World Your friends attended this lecture already and liked it. Therefore, we would like to recommend it to you ! - Introduction - Introduction Bayesian Networks Bayesian Networks Advanced I WS 06/07 Activity Recognition [Fox et al. IJCAI03] Will you go to the AdvancedAI lecture or will you visit some friends in a cafe? Lecture Hall Cafe - Introduction - Introduction
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Bayesian Networks- Intro -
Wolfram Burgard, Luc De Raedt, Kristian Kersting, Bernhard Nebel
Albert-Ludwigs University Freiburg, Germany
PCWP CO
HRBP
HREKG HRSAT
ERRCAUTERHRHISTORY
CATECHOL
SAO2 EXPCO2
ARTCO2
VENTALV
VENTLUNG VENITUBE
DISCONNECT
MINVOLSET
VENTMACHKINKEDTUBEINTUBATIONPULMEMBOLUS
PAP SHUNT
ANAPHYLAXIS
MINOVL
PVSAT
FIO2
PRESS
INSUFFANESTHTPR
LVFAILURE
ERRBLOWOUTPUTSTROEVOLUMELVEDVOLUME
HYPOVOLEMIA
CVP
BP
Mainly based on F. V. Jensen, „Bayesian Networks and Decision Graphs“, Springer-Verlag New York, 2001.
AdvancedI WS 06/07
Bay
esia
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etw
orks
Bay
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etw
orks
AdvancedI WS 06/07 Why bother with uncertainty?
Uncertainty appears in many tasks
– Partial knowledge of the state of the world
– Noisy observations
– Phenomena that are not covered by ourmodels
– Inherent stochasticity - Int
rodu
ctio
n- I
ntro
duct
ion
Bay
esia
n N
etw
orks
Bay
esia
n N
etw
orks
AdvancedI WS 06/07 Recommendation Systems
Real World
Your friends
attended this
lecture already
and liked it.
Therefore, we
would like to
recommend it
to you !
- Int
rodu
ctio
n- I
ntro
duct
ion
Bay
esia
n N
etw
orks
Bay
esia
n N
etw
orks
AdvancedI WS 06/07
Activity Recognition[Fox et al. IJCAI03]
Will you go to the
AdvancedAI lecture
or
will you visit some friends
in a cafe?
Lecture Hall
Cafe
- Int
rodu
ctio
n- I
ntro
duct
ion
Bay
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etw
orks
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AdvancedI WS 06/07
3D Scan Data Segmentation[Anguelov et al. CVPR05, Triebel et al. ICRA06]
How do you recognize the lecture hall?
- Int
rodu
ctio
n- I
ntro
duct
ion
Bay
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orks
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AdvancedI WS 06/07 Duplicate Identification
Real World
• L. D. Raedt
• L. de Raedt
• Luc De Raedt
• Wolfram Burgard
• W. Burgold
• Wolfram Burgold
- Int
rodu
ctio
n- I
ntro
duct
ion
Bay
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etw
orks
Bay
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etw
orks
AdvancedI WS 06/07
Video event recognition[Fern JAIR02,IJCAI05]
• What is going on?
• Is the red block on top of the green one?
• …
Bay
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orks
Bay
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AdvancedI WS 06/07 How do we deal with uncertainty?
• Implicit:
– Ignore what you are uncertain if you can
– Build procedures that are robust to uncertainty
• Explicit:
– Build a model of the world that describesuncertainty about its state, dynamics, andobservations
– Reason about the effects of actions given themodel
Graphical models = explicit, model-based
- Int
rodu
ctio
n- I
ntro
duct
ion
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AdvancedI WS 06/07 Probability
• A well-founded framework for uncertainty
• Clear semantics: joint prob. distribution
• Provides principled answers for:
– Combining evidence
– Predictive & Diagnostic reasoning
– Incorporation of new evidence
• Intuitive (at some level) to human experts
• Can automatically be estimated from data
- Int
rodu
ctio
n- I
ntro
duct
ion
Bay
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AdvancedI WS 06/07 Joint Probability Distribution
• „truth table“ of set of random variables
• Any probability we are interested in can becomputed from it