Establishes measure-theoretic foundations for NML in regular non-smooth models and introduces the PDL-PPMH geometric MCMC sampler to compute stochastic complexity exactly.
The decomposed normalized maximum likelihood code-length criterion for selecting hier- archical latent variable models
2 Pith papers cite this work. Polarity classification is still indexing.
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Rm-NML is introduced as a geometrically invariant extension of NML to Riemannian manifolds, with explicit computation shown for normal distributions on hyperbolic spaces.
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The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling
Establishes measure-theoretic foundations for NML in regular non-smooth models and introduces the PDL-PPMH geometric MCMC sampler to compute stochastic complexity exactly.
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Normalized Maximum Likelihood Code-Length on Riemannian Data Spaces
Rm-NML is introduced as a geometrically invariant extension of NML to Riemannian manifolds, with explicit computation shown for normal distributions on hyperbolic spaces.