========================== README (exs) ==========================
Files/Directories:
README ... this file
direction.psm ... the first example in the user's manual
dcoin.psm ... simple program modeling two Bernoulli trial processes
bloodABO.psm ... ABO blood type program (ABO gene model)
bloodAaBb.psm ... ABO blood type program (AaBb gene model)
bloodtype.dat ... data file for bloodABO.psm and bloodAaBb.psm
alarm.psm ... Bayesian network program
sbn.psm ... Singly connected Bayesian network program
hmm.psm ... discrete hidden Markov model
phmm.psm ... profile hmm for the alignment of amino-acid sequences
phmm.dat ... data file for phmm.psm
pdcg.psm ... PCFG program for top-down parsing
pdcg_c.psm ... PCFG program for Charniak's example
plc.psm ... probabilistic left-corner parsing
votes.psm ... cross-validation of naive Bayes with the `votes' data
jtree/ ... Bayesian network program in a junction-tree form
noisy_or/ ... Bayesian network program using noisy OR
How to use:
All programs are self-contained, hopefully. Try first a sample
session in each program to get familiar with a model.
Comment:
The above programs contain no negation. When a program contains
negation, you have to compile away negation by FOC (first order
compiler). For PRISM programs with negation, see ../exs_fail.
References:
(PRISM)
Parameter Learning of Logic Programs for Symbolic-statistical Modeling,
Sato,T. and Kameya,Y.,
Journal of Artificial Intelligence Research 15, pp.391-454, 2001.
New advances in logic-based probabilistic modeling by PRISM,
Sato,T. and Kameya,Y.,
Probabilistic Inductive Logic Programming, LNCS 4911, Springer,
pp.118-155, 2008.
(PCFGs)
Foundations of Statistical Natural Language Processing,
Manning,C.D. and Schutze,H.,
The MIT Press, 1999.
A Separate-and-Learn Approach to EM Learning of PCFGs
Sato,T., Abe,S., Kameya,Y. and Shirai,K.,
Proc. of the 6th Natural Language Processing Pacific Rim Symposium
(NLRPS-2001), pp.255-262, 2001.
(BNs)
Probabilistic Reasoning in Intelligent Systems,
Pearl,J.,
Morgan Kaufmann, 1988.
Expert Systems and Probabilistic Network Models,
Castillo,E., Gutierrez,J.M. and Hadi,A.S.,
Springer-Verlag, 1997.
(HMMs)
Foundations of Speech Recognition,
Rabiner,L.R. and Juang,B.,
Prentice-Hall, 1993.
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