Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.
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A new class of priors on context trees is defined by node-weighting functions, enabling exact marginal likelihoods and Bayes factors via extensions of CTW and CTM algorithms.
Derives closed-form expressions for the score and observed Fisher information matrix in a noisy Gaussian random walk HMM via Oakes' identity and forward-backward algorithm.
citing papers explorer
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Adaptive Kernel Selection for Kernelized Diffusion Maps
Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.
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Context Tree Prior Distributions based on Node Weighting with exact Bayes Factors
A new class of priors on context trees is defined by node-weighting functions, enabling exact marginal likelihoods and Bayes factors via extensions of CTW and CTM algorithms.
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Observed Fisher Information in hidden Markov models - Application to a noisy Gaussian random walk
Derives closed-form expressions for the score and observed Fisher information matrix in a noisy Gaussian random walk HMM via Oakes' identity and forward-backward algorithm.