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On Sparse‚ Spectral and Other Parameterizations of Binary Probabilistic Models

Abstract:

This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, including the Ising, generalized Ising, canonical and full parameterizations. We also discuss a parameterization that we call the ``spectral parameterization'', which has received significantly less coverage in existing literature. We provide this parameterization with a spectral interpretation by casting log-linear ...

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Journal:
Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS)
Volume:
22
Pages:
173-181
Publication date:
2012-01-01
URN:
uuid:e762732b-a3f3-445c-9064-55128546dfbf
Local pid:
cs:7214

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