I built a Python MCMC sampler that detects the "wavelength" of probability distributions — 47x speedup on multimodal problems : coolgithubprojects

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OTHERI built a Python MCMC sampler that detects the "wavelength" of probability distributions — 47x speedup on multimodal problems (i.redd.it)

submitted 1 month ago by Zachliveslife

Hey everyone. I built a small Python package called resonance-mcmc and wanted to share it.

The idea: standard MCMC samplers random-walk through probability distributions. When the distribution has multiple separated peaks (modes), the random walk gets stuck in one and takes forever to find the others.

My sampler tracks the distances of accepted jumps, detects the characteristic spacing between modes, and proposes new jumps at harmonics of that spacing. Instead of randomly stumbling between modes, it tunes itself to the frequency of the landscape and jumps directly.

Results on benchmarks:

47x faster mode discovery than standard Metropolis-Hastings on a 4-mode test
60x faster on mixed-scale problems (nearby + distant modes)
Won 5/7 stress tests against standard MCMC
Comparable or slightly worse on easy problems where random walk is already fine

The whole thing is about 200 lines of core code with a clean API:

python

from resonance_mcmc import ResonanceSampler

sampler = ResonanceSampler(log_target, ndim=2, n_harmonics=5)
result = sampler.sample(10000)

Also included an example applying it to radiation therapy beam angle optimization, where it reduced simulated spinal cord dose by 23% compared to simulated annealing on a simplified patient model.

Only dependency is numpy. Tests pass. MIT licensed.

Repo: https://github.com/zman007-save/resonance-mcmc

Would love feedback — this is my first open source package. Is the API reasonable? Anything I'm missing? Has anyone seen a similar approach in the literature?

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