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You can contribute this section with improved models or new ones. Please send them to Manfred Jaeger

Models marked "Leuven exercise" are contributed by Bruynooghe et (many!) al. from Leuven University. See the ILP 2009 paper "An Exercise with Statistical Relational Learning Systems".

  • Bloodtype
ModelCommentsExample queries
bloodtype.blpReference model. blood_founder.blp in Balios distribution (one cpt row corrected) .P(bloodtype(linus))=[a:0.32,b:0.31,ab:0.20,null:0.16]
P(bloodtype(linus)|pchrom(uwe)=a,mchrom(uwe)=a)=[a:0.4391,b:0.2092,ab:0.2354,null:0.1163] (approximate values: likelihood sampling with sample size 50000)
bloodtype.mln bloodtype.db Bloodtype encoding for Alchemy. Some initial Results, and Feedback from Alchemy developer team.
bloodtype.psmBloodtype encoding for Prism.P(bloodtype(linus))=[0.32,0.32,0.20,0.16] (exact inference)
bloodtype.rbn
bloodtype.rst
Bloodtype encoding for Primula. P(bloodtypeA(linus))=0.3218
P(bloodtypeB(linus))=0.3208
P(bloodtypeAB(linus))=0.1978
P(bloodtype0(linus))=0.1594
P(bloodtypeA(linus)|mchromA(uwe),pchromA(uwe))=0.4386
P(bloodtypeB(linus)|mchromA(uwe),pchromA(uwe))=0.2130
P(bloodtypeAB(linus)|mchromA(uwe),pchromA(uwe))=0.2365
P(bloodtype0(linus)|mchromA(uwe),pchromA(uwe))=0.1117
(exact inference; numerical inaccuracies due to rounding in binarization of multi-valued predicates)
In order to create relational structures for this domain we have developed a Pedigree generator. This program has different options which can be set by command-line parameters or by a configuration file. In that archive you can find the executable program, some configuration files and the output for the different systems and even in RDEF.
  • University
ModelCommentsExample queries
university.mlnReference model. P(advisedBy(Gail,Glen))=?
P(inPhase(Hanna,Pre_Quals))=?
P(inPhase(Hanna,Post_Quals))=?
More detailed Results and Feedback from Alchemy developer team.
university.blp
university-blp-evidence.txt
University encoding for Balios. Model must be conditioned on evidence in university-blp-evidence.txt P(advisedBy(Gail,Glen))=?
P(inPhase(Hanna,Pre_Quals))=?
P(inPhase(Hanna,Post_Quals))=?
Details
university.rbn
university.rst
University encoding for Primula. Model must be conditioned on the same evidence as the blp model. Evidence must be entered through the Primula inference module (no read-in from file possible) P(advisedBy(Gail,Glen))=0.11
P(inPhase(Hanna,Pre_Quals))=0.1675
P(inPhase(Hanna,Post_Quals))=0.8297
Details
  • HMM
ModelCommentsExample queries
multistatehmm8.blp

multistatehmm16.blp

multistatehmm32.blp

multistatehmm64.blp

A simple HMM model for a random walk on a line with 8,16,32,64 positions (= number of hidden states), and a 2-state observable 8 states: P(hiddenstate(10)|position(5)=left)=
[s1:0.21,s2:0.20,s3:0.19,s4:0.15,s5:0.11,s6:0.08,s7:0.05,s8:0.03]
multistatehmm8.psm

multistatehmm16.psm

multistatehmm32.psm

multistatehmm64.psm

multistatehmm8.rbn

multistatehmm16.rbn

multistatehmm32.rbn

multistatehmm64.rbn

8 states: P(hiddenstate(10)|position(5)=left)=
[s1:0.2204,s2:0.2040,s3:0.1763,s4:0.1424,s5:0.1074,s6:0.0737,s7:0.046,s8:0.0298]
  • Noisy-or
ModelComments Example queries
noisy-or.rbn
large.rst,small.rst
Reference model. Very simple noisy-or problem for Primula in two scenarios, large and small. P(on(3))=0.81
P(on(3)|!on(0))=0.67 (small scenario)
noisy-or_large.psm Noisy-or encoding for Prism with the large scenario.
noisy-or_large_sample.psm Noisy-or encoding for Prism with the large scenario with a sampling facility for approximate inference.
noisy-or_small.psm Noisy-or encoding for Prism with the small scenario. P(on(3))=0.81
noisy-or_small.blp Noisy-or encoding for Balios with the small scenario. P(on(3))=0.81
P(on(3)|!on(0))=0.67
  • Real Estate (Leuven Exercise)
ModelCommentsExample queries
realestate.mln
realestate_example.db
MLN encoding for real estate and one possible database
realestate.blpBLP for the `real-estate' entity-relationship model. The hard constraint that each house is bought by at most one customer is not modelled.P(buys(c1,h2)) = [no:0.47,yes:0.53]
P(buys(c1,h2)|rich(c1)=no,cheap(h2)=no) = [no:0.744,yes:0.256]
realestate.blogrealestate encoding BLOG
realestate.yaprealestate encoding for CLP(BN)P(buys(B,gates,villa)|cheap(no,villa))=?
realestate.ibl Implementation of the realestate Domain. Remark: The implementation has several Problems. First it does not keep track of the result of queries. this means that for example wants(customer,facility) will lead in two different calls in two different results. Second it is not possible to pose any usefull query as almost all give "Fatal error: exception Stack_overflow". e.g. fac1=facility() price(Cons(fac1,Nil)) works, but when calling this function via house(Cons(fac1,Nil)) it crashes
realestate.psm
realestate_task1_extra.psm
PRISM encoding
realestate.rbn
realestate.rst
realestate encoding PRIMULAP(expensive(house2)) = 0.1865
P(expensive(house2) | has(house2,swimmingPool)) = 0.4024 M
Sampling-based inference implemented in Primula.
  • Weather Markov Model (Leuven Exercise)
ModelCommentsExample queries
weatherhmm_task1.mlnAlchemy
weatherhmm_task1.blpBalios
weathehrmm_task1.blogBLOG
weatherhmm_task1.yapCLP(BN)
weatherhmm_task1.ibl IBAL
weatherhmm_task1.psmPRISM encoding
weatherhmm_task1.rbnPRIMULA encoding
  • Weather Hidden Markov Model (Leuven Exercise)
ModelCommentsExample queries
weatherhmm_task2.mlnAlchemy
weatherhmm_task2.blpBalios
weathehrmm_task2.blogBLOG
weatherhmm_task2.yapCLP(BN)
weatherhmm_task2.ibl IBAL
weatherhmm_task2.psmPRISM encoding
weatherhmm_task2.rbnPRIMULA encoding
  • Weather Hidden Markov Model Where Umbrella Influences the Weather (Leuven Exercise)
ModelCommentsExample queries
weatherhmm_task3.mlnAlchemy
weatherhmm_task3.blpBalios
weathehrmm_task3.blogBLOG
weatherhmm_task3.yapCLP(BN)
weatherhmm_task3.ibl IBAL
weatherhmm_task3.psmPRISM encoding
weatherhmm_task3.rbnPRIMULA encoding
  • Weather Hidden Markov Model With Several Guards (Leuven Exercise)
ModelCommentsExample queries
weatherhmm_task4.mlnAlchemy
weatherhmm_task4.blpBalios
weathehrmm_task4.blogBLOG
weatherhmm_task4.yapCLP(BN)
weatherhmm_task4.ibl IBAL
weatherhmm_task4.psm
weatherhmm_task4_extra1.psm
weatherhmm_task4_extra2.psm
PRISM encoding
weatherhmm_task4.rbnPRIMULA encoding
  • Weather Hidden Markov Model With Several Guards (Leuven Exercise)
ModelCommentsExample queries
weatherhmm_task5.mlnAlchemy
weatherhmm_task5.blpBalios
weathehrmm_task5.blogBLOG
weatherhmm_task5.yapCLP(BN)
weatherhmm_task5.ibl IBAL
weatherhmm_task5.psm
weatherhmm_task4_extra1.psm
weatherhmm_task4_extra2.psm
PRISM encoding
weatherhmm_task5.rbnPRIMULA encoding