========================== 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.
