The WinMine
Toolkit
Max
Chickering (Microsoft Research)
The WinMine Toolkit is set of tools that allow you to
build Bayesian networks and dependency networks from data. These tools, which
are for academic use, can be downloaded for free off of the web. For my
presentation, I will be available to show those interested how to use the
tools.
SMILE
(Structural Modeling, Inference, and Learning Engine) is a fully portable
library of C++ classes implementing graphical decision-theoretic methods, such
as Bayesian networks and influence
diagrams, directly amenable to inclusion in intelligent systems. Its Windows user interface, GeNIe is a
versatile and user-friendly development environment for graphical
decision-theoretic models. Both
modules, developed at the Decision Systems Laboratory, University of
Pittsburgh, have been made available to the community in July 1998 (the
download site is http://www.sis.pitt.edu/~genie).
B-Course, a
web-based data analysis tool
Petri
Myllymaki, Petri Kontkanen, Hannes Wettig (Complex Systems Computation Research
Group, Helsinki Institute for Information Technology)
B-Course is a web-based tool for Bayesian and causal
modeling. It can be used as a data analysis tool for any research where
dependence, classification or causal models based on sample data are of
interest. B-Course can also be used as an interactive tutorial, which provides
you with data sets that have been prepared in advance. B-Course can be freely
used for educational and research purposes at address http://b-course.hiit.fi.
A class of
location-based decisioning tasks that is now feasible via an MDP approach
Mark E.
Plutowski (Cyber Technologies Laboratory, Sony Electronic Inc.)
I evaluated four MDP solvers upon a class of stochastic
planning tasks drawn from a location-based application scenario. The problems
involve generating the optimal visitation sequence across a number of
geographically located "stores" at which one or more valuable
resources are located with some probability, governed by a utility function
based on travel times and acquisition rewards. Two of these solvers use a
factored MDP approach that mitigates the state space explosion problem by
lumping states together by value. The algorithm underlying these two techniques
is based upon a dynamic Bayesian network formulation, utilizing the algebraic
decision diagram (ADD) as the primary data structure. A third solver utilizes a
symbolic heuristic approach that utilizes the same data structures, but which
obtains greater efficiencies by ignoring unreachable states during the forward
expansion phase of value iteration. A fourth solver utilizes a full
reachability forward expansion phase starting from a given set of states. I
settled upon the symbolic heuristic approach as the main MDP solver, using the
other three for computing the heuristic. The approach exploits the strong
reachability structure of the task in the heuristic as well as the value
function computation. I conclude that it is now feasible to attack moderately
sized task instances from this class of problems. I discuss some practical
lessons learned along the way in applying these algorithms and identify some
new challenges.
We
introduce a software environment to support research and development in the
area of both deterministic and non-deterministic reasoning. This environment
–REES (Reasoning Engine(s) Evaluation Shell) has a plug-in oriented
architecture that promotes reuse of existing software components and allows for
the comparison and evaluation of alternative technologies. The third release of
the REES system and technical documentation is now available at http://www.ics.uci.edu/~radum/rees.html.
PRISM: A
first order modeling system
Taisuke
Sato (Tokyo Institute of Technology / CREST JST)
Neng-Fa
Zhou (The City University of New York)
PRISM1.6 is a symbolic-statistical modeling language
for modeling complex phenomena. It is
an extension of logic programming language B-Prolog that offers the user (1)
probabilistic built-ins for constructing logical-probabilistic models in terms
of logic programs and (2) a fast EM learner, called graphical EM, that learns
parameters embedded in programs from observed facts. We are applying PRISM1.6 to several applications including
statistical natural language processing (HPSG), modeling Shogi (Japanese chess)
players, and linkage analysis.
Richard
Scheines, Joe Ramsey, Matt Easterday (Carnegie Mellon University)
Tetrad 4 supports the construction and discovery of Bayes
Nets, Structural Equation Models, and Time Series Models (in particular genetic
regulatory networks with microarray measurements). Algorithms include the PC
algorithm, the FCI algorithm, the CCD algorithm (linear cyclic models), and a
genetic algorithm for linear DAGs. Causality Lab is software for simulating the
scientific process of investigating causal hypotheses. Based on a stored but
hidden Bayes Net, this software allows the user to set-up and carry out passive
observational studies and randomized trials, judge the independence relations
that hold in such studies, hypothesize causal models that would explain such
evidence, and compare predictions against the evidence.
http://www.phil.cmu.edu/projects/tetrad_download/
http://www.phil.cmu.edu/projects/causality-lab/
VIBES: A
Variational Inference Engine for Bayesian Networks
John Winn
(Cambridge University)
Christopher
M. Bishop (Microsoft Research, Cambridge)
In recent years variational methods have become a
popular tool for approximate inference and learning in a wide variety of
probabilistic models. For each new application, however, it is currently
necessary first to derive the variational update equations, and then to
implement them in application-specific code. Each of these steps is both time
consuming and error prone. In this presentation we demonstrate a general-purpose
inference engine called VIBES (`Variational Inference for Bayesian Networks'),
which allows a wide variety of probabilistic models to be implemented and
solved variationally without recourse to coding. New models are specified via a
graphical interface analogous to a drawing package. VIBES then automatically
generates and solves the variational equations. We illustrate the power and
flexibility of VIBES using examples from Bayesian mixture modelling.