Reactive graphs enable efficient MCMC inference in probabilistic programming languages by automatically tracking and selectively recomputing data dependencies during sampling.
The No-U-turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
A nonlinear analytical theory derived via asymptotic analysis identifies four dynamical regimes for heterogeneous chemotactic cell collectives and predicts a balanced mixing-localization regime for dendritic and T cell co-migration enabled by strong chemoattractant consumption.
K-essence cosmology induces a redshift-dependent effective mass on gravitational waves, causing phase shifts that link scalar field dynamics to GW observables without changing wave speed or luminosity distance.
citing papers explorer
-
Reactive Graphs for Efficient Markov Chain Monte Carlo Inference in Probabilistic Programming Languages
Reactive graphs enable efficient MCMC inference in probabilistic programming languages by automatically tracking and selectively recomputing data dependencies during sampling.