Dear colleagues,

We are  pleased to  announce the  release of PRISM2.3  which is
available for download at:

PRISM is a  probabilistic extension of Prolog that  has been developed
as a generic  interface between users and  probabilistic modeling. Not
only does it support generative  modeling by offering a dazzling array
of built-ins for Bayesian and non-Bayesian inference, it also provides
built-ins  for discriminative  modeling  such as  CRFs  and those  for
modeling   cyclic  relations   that  lead   to  infinite   probability

The  latest  PRISM,  PRISM2.3,  further adds  a  unique  mechanism  of
ranking-based parameter learning. Now the user can learn probabilities
by giving the  system a ranking of propositions  and obtain parameters
such  that  high-rank  propositions  have  higher  probabilities  than
low-rank  propositions.    This  characteristic  is  applied to   the
situation where ranking is more important than probability itself such
as user preference and information retrieval.

We believe that PRISM2.3 now offers the widest flexibility of parameter
learning for logic-based probabilistic modeling for Bayesian and
non-Bayesian users.

With Best Regards,

Taisuke Sato, Yoshitaka Kameya, Ryosuke Kojima, Neng-Fa Zhou
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