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