Quenched Ensemble Sampling
Quenched Ensemble Sampling generalizes nested sampling's hard energy constraint to repulsive potentials, traversing first-order phase transitions where tempering fails.
Quenched Ensemble Sampling generalizes nested sampling's hard energy constraint into a family of repulsive potentials at the energy boundary, preserving monotone energy descent while making the constrained target amenable to scalable gradient-based kernels. On synthetic phase-transition models it estimates marginal likelihood and draws posterior samples across first-order transitions where popular alternatives such as tempering fail. Applications include marginal likelihood estimation for Bayesian neural network architecture comparison and partition function estimation in a high-dimensional continuous lattice field theory.
- Repulsive potentials replace nested sampling's hard constraint, enabling gradient-based kernels
- Estimates marginal likelihood and posteriors across first-order transitions where tempering fails
- Applied to Bayesian neural network model comparison
- Traverses first-order transition and estimates partition function in lattice field theory
From the original article
1 min read at arxiv.orgSome of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling algorithms stall.
By David Yallup · arxiv.org
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