The Maximum Entropy Modeling Toolkit (MEMT)
supports parameter estimation and prediction for maximum entropy
models in discrete domains. The maximum entropy framework provides a constructive method for obtaining
the unique conditional distribution p*(y|x) that satisfies a set of linear constraints and maximizes the
conditional entropy H(p|f) with respect to the empirical distribution f(x).
The maximum entropy distribution
p*(y|x) also has a unique parametric representation in the class of exponential models, as m(y|x) = r(y|x) / Z(x)
where the numerator r(y|x) is a product of exponential weights
r(y|x) = prod_i alpha_i^g_i(x,y)
for alpha_i = exp(lambda_i), and the denominator Z(x) = sum_y r(y|x) is required to satisfy the axioms of probability.
Current Version: 1.5
License Type: Free
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