From probp@probp.com  Fri May 28 21:19:02 2004
From: probp@probp.com (Afany Software)
Date: Fri, 28 May 2004 16:19:02 -0400
Subject: [bp-users]Release of PRISM 1.7
Message-ID: <011b01c444f1$0568d520$def9f592@ZHOU>

Release of PRISM1.7

We are pleased to announce the release of PRISM1.7,
which is available for download at:

 http://sato-www.cs.titech.ac.jp/prism/.

PRISM is  a logic-based probabilistic  language and
is easy to learn and use for anyone who is familiar
with Prolog.

PRISM  is suitable  for modeling  those statistical
phenomena   that   are   governed  by   rules   and
probabilities such  as statistical natural language
processing, game analysis, data mining, performance
tuning,  and bio-sequence  analysis. PRISM  has the
following unique features:

(1) The   user   can   use   programs   to   define
    distributions over terms and atoms.

Mathematically a  PRISM program is  a formal object
which defines a probability measure over the set of
possible     Herbrand     interpretations.      The
distributions  are derived  and  computed from  the
defined  measure.   There  are no  restrictions  on
programs,  e.g., programs  are not  required  to be
range-restricted or Datalog programs.

(2) Parameters   in   a   program   are   learnable
    automatically from examples.

A  PRISM  program  contains statistical  parameters
that  reflect  the  statistical properties  of  the
model.  They can  be  automatically estimated  from
examples  by  ML  (Maximum  Likelihood)  estimation
performed by a built-in EM learning routine.

(3) Probabilities  are  computed  efficiently in  a
    dynamic programming manner.

PRISM   uses   "explanation   graphs"  to   compute
probabilities and learn parameters, where solutions
are shared  as in dynamic  programming. Explanation
graphs are constructed by tabled search.

(4) PRISM  is a high  level yet  efficient modeling
    language.

Popular symbolic-statistical  models such as hidden
Markov models,  probabilistic context free grammars
and Bayesian  nets can be  described in PRISM  in a
very compact way. Their parameter learning in PRISM
can be done as  efficiently as those by specialized
EM algorithms such as the Baum-Welch algorithm.  In
addition,  PRISM  can  be  used  to  model  certain
phenomena  that   are  hard  to   model  using  the
specialized statistical tools.

PRISM1.7 is  the latest version of  PRISM, which is
implemented  on top  of B-Prolog and makes  use of
B-Prolog's  efficient linear tabling  mechanism for
tabled  search. This  version is  considerably more
efficient in both time  and space than the previous
version,   PRISM1.6,  thanks  to   improved  tabled
search.  It   also  provides  new   built-ins  that
facilitate  modeling   and  learning.  PRISM1.7  is
sustainable  to relatively large  sets of  data and
would be  of interest to  anyone who would  like to
challenge    statistical   modeling    of   complex
phenomena.

With best regards,

Taisuke Sato (Tokyo Institute of Technology)
and
Neng-Fa Zhou (The City University of NewYork)