Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
Effective sample size is a standard summary of Markov chain Monte Carlo output, but it is usually attached to scalar or Euclidean summaries chosen by the analyst. For manifold-valued samples this choice is not canonical: coordinate-wise effective sample sizes can change under rotations, chart changes, or alternative embeddings of the same underlying path. We propose an intrinsic effective sample size based on kernel discrepancy. The proposed quantity is the number of independent draws that would yield the same expected squared kernel discrepancy between the empirical distribution and the target distribution. This gives an exact finite-sample risk interpretation, an asymptotic integrated-a...
| Authors | Kisung You |
|---|---|
| Primary category | stat.ML |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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