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Bayesian-Like Estimation with Unnormalized Model
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AI
DOI：10.1007/978-3-032-03918-7_36.png)
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Abstract
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Parameter estimation of probabilistic models for discrete variables is often infeasible due to the calculation of the normalization constant required to ensure the model represents a valid probability distribution, and various approaches have been developed to resolve this problem. In this paper, we consider a computationally feasible estimator for discrete probabilistic models based on a concept of empirical localization. Furthermore, we propose a computationally feasible estimator similar to the MAP estimator in Bayesian estimation by extending the above estimator.
Keywords:
Bregman divergence
Unnormalized model
MAP estimator
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