gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
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UNVERDICTED 16representative citing papers
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
Diffusion model priors enable training-free Bayesian sampling for more accurate rain field reconstruction from path-integrated commercial microwave link measurements than Gaussian process baselines.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Transformer networks sample up to 180x180 2D Ising systems and 64x64 Edwards-Anderson systems by generating spin groups with probability approximations, yielding ~20x higher effective sample size than prior neural samplers at criticality.
Statistical analysis of energy data falsifies the 1% exponential growth in the Kardashev model, shows linear extrapolation yields a 1.6E15-year Type II timescale, and introduces the KSN renormalization B(t) = P(t)/H(t) spanning 14 orders of magnitude.
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
FluxMC integrates flow matching with parallel tempering MCMC to converge in under five hours on high-fidelity IMRPhenomHM waveforms for massive black hole binaries, where standard methods fail after hundreds of hours and produce two to three orders of magnitude higher distributional error.
Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match known patterns.
Turing patterns on non-fluctuating lattices under mechanical stress modeled by Finsler geometry respond to external forces similarly to those on fluctuating membranes.
Simulations of the 3D Hubbard-Holstein model at half-filling find antiferromagnetic and charge-ordered insulators separated by a first-order transition with no intervening metal, plus metallic bipolaronic states and pseudogap features at higher temperatures.
Bayesian inference reconstructs bathymetry from point water height measurements, improving NRMSE over adjoint optimization on real wave flume data while quantifying uncertainty.
Probabilistic host-star assignments via asterodensity profiling suggest the exoplanet radius gap is less empty in binary systems once possible circumsecondary planets are included.
Simulations show two-party systems moderate policy positions while multiparty systems increase polarization, with turnout and activists further driving extremes.
An eigenvalue-based small-sample approximation to MCMC reduces required paths from up to 1,000,000 to as few as 10 while producing comparable steady-state distributions by Wasserstein distance and lower variance.
Quantum effects govern behavior in warm dense matter and inertial fusion plasmas and are best modeled by combining quantum methods through downfolding from first-principles simulations.
citing papers explorer
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gemlib.mcmc: composable kernels for Metropolis-within-Gibbs sampling schemes
gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
-
Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
-
Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Diffusion model priors enable training-free Bayesian sampling for more accurate rain field reconstruction from path-integrated commercial microwave link measurements than Gaussian process baselines.
-
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
-
Sampling two-dimensional spin systems with transformers
Transformer networks sample up to 180x180 2D Ising systems and 64x64 Edwards-Anderson systems by generating spin groups with probability approximations, yielding ~20x higher effective sample size than prior neural samplers at criticality.
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Kardashev's Conundrum: Statistical Falsification of the Standard Kardashev Model and the Kardashev--Sagan--Nakamoto Resolution
Statistical analysis of energy data falsifies the 1% exponential growth in the Kardashev model, shows linear extrapolation yields a 1.6E15-year Type II timescale, and introduces the KSN renormalization B(t) = P(t)/H(t) spanning 14 orders of magnitude.
-
Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
-
FluxMC: Rapid and High-Fidelity Inference for Space-Based Gravitational-Wave Observations
FluxMC integrates flow matching with parallel tempering MCMC to converge in under five hours on high-fidelity IMRPhenomHM waveforms for massive black hole binaries, where standard methods fail after hundreds of hours and produce two to three orders of magnitude higher distributional error.
-
Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match known patterns.
-
Turing patterns on non-fluctuating surfaces under mechanical stresses
Turing patterns on non-fluctuating lattices under mechanical stress modeled by Finsler geometry respond to external forces similarly to those on fluctuating membranes.
-
Magnetotransport and Phase competition in three-dimensional Hubbard-Holstein model at half-filling
Simulations of the 3D Hubbard-Holstein model at half-filling find antiferromagnetic and charge-ordered insulators separated by a first-order transition with no intervening metal, plus metallic bipolaronic states and pseudogap features at higher temperatures.
-
Bathymetry Reconstruction by Bayesian Inference
Bayesian inference reconstructs bathymetry from point water height measurements, improving NRMSE over adjoint optimization on real wave flume data while quantifying uncertainty.
-
Determining the Host Stars of Planets in Binary Star Systems with Asterodensity Profiling: Investigating the Canonical Radius Gap
Probabilistic host-star assignments via asterodensity profiling suggest the exoplanet radius gap is less empty in binary systems once possible circumsecondary planets are included.
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A computational model of spatial politics: Hotelling-Downs model as statistical physics
Simulations show two-party systems moderate policy positions while multiparty systems increase polarization, with turnout and activists further driving extremes.
-
Fast Monte-Carlo
An eigenvalue-based small-sample approximation to MCMC reduces required paths from up to 1,000,000 to as few as 10 while producing comparable steady-state distributions by Wasserstein distance and lower variance.
-
Quantum effects in plasmas
Quantum effects govern behavior in warm dense matter and inertial fusion plasmas and are best modeled by combining quantum methods through downfolding from first-principles simulations.