BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

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+
+<h2><a name="features">Major features</h2> 
+<ul> 
+ 
+<li> BNT supports many types of
+<b>conditional probability distributions</b> (nodes),
+and it is easy to add more.
+<ul> 
+<li>Tabular (multinomial)
+<li>Gaussian
+<li>Softmax (logistic/ sigmoid)
+<li>Multi-layer perceptron (neural network)
+<li>Noisy-or
+<li>Deterministic
+</ul> 
+<p> 
+ 
+<li> BNT supports <b>decision and utility nodes</b>, as well as chance
+nodes,
+i.e., influence diagrams as well as Bayes nets.
+<p> 
+ 
+<li> BNT supports static and dynamic BNs (useful for modelling dynamical systems
+and sequence data).
+<p> 
+ 
+<li> BNT supports many different <b>inference algorithms</b>,
+and it is easy to add more.
+ 
+<ul> 
+<li> Exact inference for static BNs:
+<ul> 
+<li>junction tree
+<li>variable elimination
+<li>brute force enumeration (for discrete nets)
+<li>linear algebra (for Gaussian nets)
+<li>Pearl's algorithm (for polytrees)
+<li>quickscore (for QMR)
+</ul> 
+ 
+<p> 
+<li> Approximate inference for static BNs:
+<ul> 
+<li>likelihood weighting
+<li> Gibbs sampling
+<li>loopy belief propagation
+</ul> 
+ 
+<p> 
+<li> Exact inference for DBNs:
+<ul> 
+<li>junction tree
+<li>frontier algorithm
+<li>forwards-backwards (for HMMs)
+<li>Kalman-RTS (for LDSs)
+</ul> 
+ 
+<p> 
+<li> Approximate inference for DBNs:
+<ul> 
+<li>Boyen-Koller
+<li>factored-frontier/loopy belief propagation
+</ul> 
+ 
+</ul> 
+<p> 
+ 
+<li> 
+BNT supports several methods for <b>parameter learning</b>,
+and it is easy to add more.
+<ul> 
+ 
+<li> Batch MLE/MAP parameter learning using EM.
+(Each node type has its own M method, e.g. softmax nodes use IRLS,<br> 
+and each inference engine has its own E method, so the code is fully modular.)
+ 
+<li> Sequential/batch Bayesian parameter learning (for fully observed tabular nodes only).
+</ul> 
+ 
+ 
+<p> 
+<li> 
+BNT supports several methods for <b>regularization</b>,
+and it is easy to add more.
+<ul> 
+<li> Any node can have its parameters clamped (made non-adjustable).
+<li> Any set of compatible nodes can have their parameters tied (c.f.,
+weight sharing in a neural net).
+<li> Some node types (e.g., tabular) supports priors for MAP estimation.
+<li> Gaussian covariance matrices can be declared full or diagonal, and can
+be tied across states of their discrete parents (if any).
+</ul> 
+ 
+<p> 
+<li> 
+BNT supports several methods for <b>structure learning</b>,
+and it is easy to add more.
+<ul> 
+ 
+<li> Bayesian structure learning,
+using MCMC or local search (for fully observed tabular nodes only).
+ 
+<li> Constraint-based structure learning (IC/PC and IC*/FCI).
+</ul> 
+ 
+ 
+<p> 
+<li> The source code is extensively documented, object-oriented, and free, making it
+an excellent tool for teaching, research and rapid prototyping.
+ 
+</ul> 
+ 
+ 
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