Reproduce “Spectral partitioning for $k$-block averaging kernels of finite Markov chains” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Michael C.H. Choi, Youjia Wang
The authors develop a method that uses a Markov chain’s eigenfunctions to choose state blocks for conditional resampling. For multiple blocks, weighted k-means proposes partitions, which are selected by the original objective; this search is approximate. In Bayesian variable selection, balanced four-block resampling reduced pair-pattern estimation error 8.4-fold versus a single-toggle baseline, while mass-matched random partitions performed similarly.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.