Abstract / truncated to 115 words (read the full abstract)

This thesis investigates the iterative application of Monte Carlo methods to the problem of parameter estimation for models of maximum entropy, minimum divergence, and maximum likelihood among the class of exponential-family densities. It describes a suite of tools for applying such models to large domains in which exact computation is not practically possible. The first result is a derivation of estimators for the Lagrange dual of the entropy and its gradient using importance sampling from a measure on the same probability space or its image under the transformation induced by the canonical sufficient statistic. This yields two benefits. One is the flexibility to choose an auxiliary distribution for sampling that reduces the standard error of ... toggle 10 keywords

maximum – entropy – fitting – modeling – modelling – language – monte – carlo – sentence – divergence

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