arXiv AI
Thermodynamic Cyclic Processes with Markov Samplers in Bayesian Inference
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 10
Microcanonical Hamiltonian Monte Carlo and the Helmholtz Theorem
The paper investigates the Microcanonical Hamiltonian Monte Carlo (MHMC) algorithm from a thermodynamic perspective, deriving its state variables and potentials to show that it represents a microcanonical thermodynamic ensemble. It demonstrates analytically and numerically that MHMC satisfies the Helmholtz theorem, an alternative form of the first law of thermodynamics, and introduces a new sampling algorithm tailored for lower-dimensional inference problems. The authors conclude that canonical Markov Chain Monte Carlo methods are more natural than MHMC when evaluated through thermodynamic and information-theoretic lenses.
arXiv Machine Learning
Aug 11
Leveraging generative models to assist Monte Carlo sampling
arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.
arXiv Machine Learning
Sep 4
FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
FrOGS is a hybrid discrete neural sampler that couples an autoregressive model with a continuous-time Markov chain, trained under a single shared loss to sample alloy configurations across many chemical conditions. It produces independent, unbiased configurations, estimates the partition function, and yields consistent thermodynamic observables on a common absolute free‑energy scale. The method matches exact results for the 2D Ising model and reproduces reference phase diagrams for AgPd and CuAu, avoiding mode collapse and correctly recovering the stability range of the CuAu$_3$ phase.
arXiv Machine Learning
Jul 7
Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling
arXiv:2512. 11415v3 Announce Type: replace-cross Abstract: We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of latent-state cycles.
arXiv AI
Jun 2
Extending Causal Metamodeling to a non-Markovian Queue
arXiv:2606. 00795v1 Announce Type: cross Abstract: Metamodels for discrete-event simulations approximate the behavior of simulation models without running expensive simulations.
arXiv AI
Aug 17
FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC
arXiv:2608. 13774v1 Announce Type: new Abstract: Markov chain Monte Carlo (MCMC) requires only the ability to evaluate the likelihood, making it a common technique for inference in complex models.