Graphical Models - Inference -

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Graphical Models - Inference - 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 Advanced I WS 06/07 Most Probable Explanation

description

MINVOLSET. KINKEDTUBE. PULMEMBOLUS. INTUBATION. VENTMACH. DISCONNECT. PAP. SHUNT. VENTLUNG. VENITUBE. PRESS. MINOVL. FIO2. VENTALV. PVSAT. ANAPHYLAXIS. ARTCO2. EXPCO2. SAO2. TPR. INSUFFANESTH. HYPOVOLEMIA. LVFAILURE. CATECHOL. LVEDVOLUME. STROEVOLUME. ERRCAUTER. HR. - PowerPoint PPT Presentation

Transcript of Graphical Models - Inference -

Page 1: Graphical Models - Inference -

Graphical Models- Inference -

Graphical Models- Inference -

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

FIO2PRESS

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

Most Probable Explanation

Page 2: Graphical Models - Inference -

Bayesian Networks

Bayesian Networks

AdvancedI WS 06/07 Outline

• Introduction • Reminder: Probability theory• Basics of Bayesian Networks• Modeling Bayesian networks• Inference (VE, Junction

tree,MPE)• Excourse: Markov Networks• Learning Bayesian networks• Relational Models

Page 3: Graphical Models - Inference -

Bayesian Networks

Bayesian Networks

AdvancedI WS 06/07

∑∏b

Elimination operator

P(a|e=0)

factor B:

P(a)

P(c|a)

P(b|a) P(d|b,a) P(e|b,c)

facotr C:

factor D:

factor E:

factor A:

e=0

B

C

D

E

A

e)(a,hD

(a)hE

e)c,d,(a,hB

e)d,(a,hC

VE, Bucket elimination [Dechter ‘96]

- Inference (MPE)

- Inference (MPE)

Page 4: Graphical Models - Inference -

Bayesian Networks

Bayesian Networks

AdvancedI WS 06/07

Finding MPE [Dechter ‘96]

∏b

maxElimination operator

MPE

factor B:

P(a)

P(c|a)

P(b|a) P(d|b,a) P(e|b,c)

factor C:

factor D:

factor E:

factor A:

e=0

B

C

D

E

A

e)(a,hD

(a)hE

e)c,d,(a,hB

e)d,(a,hC

)xP(maxMPEx

=

),|(),|()|()|()(maxby replaced is

,,,,cbePbadPabPacPaPMPE

:

bcdea=

∑ max

- Inference (MPE)

- Inference (MPE)

Page 5: Graphical Models - Inference -

Bayesian Networks

Bayesian Networks

AdvancedI WS 06/07 Generating the MPE-tuple

C:

E:

P(b|a) P(d|b,a) P(e|b,c)B:

D:

A: P(a)

P(c|a)

e=0 e)(a,hD

(a)hE

e)c,d,(a,hB

e)d,(a,hC

(a)hP(a)max arga' 1. E

a⋅=

0e' 2. =

)e'd,,(a'hmax argd' 3. C

d=

)e'c,,d',(a'h

)a'|P(cmax argc' 4.B

c

××=

)c'b,|P(e')a'b,|P(d')a'|P(bmax argb' 5.

b××

×=

)e',d',c',b',(a' Return

- Inference (MPE)

- Inference (MPE)