A new RTU grid method models the lensing source as a Gaussian process on a ray-transformed uniform grid, achieving comparable fits with roughly half the pixels per dimension and higher ELBOs on mock data.
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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Mixed citation behavior. Most common role is background (67%).
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abstract
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC's performance is highly sensitive to two user-specified parameters: a step size {\epsilon} and a desired number of steps L. In particular, if L is too small then the algorithm exhibits undesirable random walk behavior, while if L is too large the algorithm wastes computation. We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps. Empirically, NUTS perform at least as efficiently as and sometimes more efficiently than a well tuned standard HMC method, without requiring user intervention or costly tuning runs. We also derive a method for adapting the step size parameter {\epsilon} on the fly based on primal-dual averaging. NUTS can thus be used with no hand-tuning at all. NUTS is also suitable for applications such as BUGS-style automatic inference engines that require efficient "turnkey" sampling algorithms.
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representative citing papers
EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.
CHIBI is a new hierarchical Bayesian method for multifrequency synthesis radio imaging based on synchrotron spectral parametrization, demonstrated on VLBA MOJAVE data and simulated EHT observations of M87*.
DREAM enables exact-gradient Bayesian calibration of nuclear models via offline SVD emulation of parameter-dependent operators, demonstrated by rapid HMC convergence on an 18-parameter CDCC analysis of d+58Ni scattering.
Large longitudinal RCT finds high rates of following AI personal advice but no sustained well-being gains versus a hobbies control condition.
Introduces HICOBIAN, a differentiable fuzzy hierarchical cosmic-web bias model using sigmoid gradients for smooth region transitions, enabling accurate Bayesian field-level reconstruction of primordial density fields validated by Fourier-space statistics.
A fully Bayesian pixel-based Doppler imaging framework uses Gaussian Process priors and Hamiltonian Monte Carlo to simultaneously infer surface maps and geometric parameters from spectral data.
Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.
A data-driven model for periodic ADC integral nonlinearity in JWST/NIRISS is fitted to ramp residuals and applied to correct the ERS1366 WASP-39b transmission spectrum, reducing systematics at the 30ppm level.
GWTC-4 data supports two mass-dependent spin subpopulations: low-mass binaries mostly slow-spinning, high-mass ones dominated by moderate-to-rapid spins with transition from 35 to 70 solar masses.
A parametrized analytical model for BBH mass ratios from the stable mass transfer channel is derived and applied to the 10 solar-mass peak in GWTC-4, favoring little mass-ratio reversal.
Compact analytic Jacobians are derived for reparameterizing Keplerian orbits between orbital elements and Cartesian states, correcting a singularity in the Skowron et al. (2011) microlensing model and improving MCMC efficiency in astrometric fitting.
The GW-galaxy cross-correlation method, unified with spectral sirens in a harmonic framework, can measure H0 to 1% and Omega_m to 5% precision with 2 years of data from next-generation detectors like Einstein Telescope and Cosmic Explorer.
Combined five-PTA dataset yields posterior on SGWB power-law amplitude and index consistent with nonzero signal but below 5-sigma significance, with reconstructed angular correlations matching the Hellings-Downs prediction.
300S stellar stream exhibits three density peaks, smooth width variations, a possible 4.7 degree gap, and a kink modeled as resulting from Large Magellanic Cloud interaction across its full known footprint.
DESI DR1 full-shape galaxy clustering constrains Omega_m = 0.296 ± 0.010, H0 = 68.63 ± 0.79 km/s/Mpc, and sigma_8 = 0.841 ± 0.034, consistent with LambdaCDM and Planck.
dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
TFMPE combines likelihood factorisation with tokenised flow matching to enable efficient hierarchical SBI from single-site simulations, producing well-calibrated posteriors at lower computational cost on a new benchmark and real models.
The transmission spectrum of AU Mic b is dominated by the transit light source effect from stellar spots, yielding only weak atmospheric constraints with a preferred scale height below 185 km.
A multimodal transformer-based generic mixture density network estimates FRB scattering timescale τ with 94% R² on measurable events and 90% recall for unresolvable cases on CHIME/FRB data.
Hierarchical Bayesian inference on GWTC-5.0 constrains the memory enhancement factor to 0.26 with large uncertainties consistent with the GR value of 1 and forecasts that 2000 detections are needed for a 1σ constraint away from zero.
StanBKT provides a unified Bayesian inference framework for BKT models supporting HMC, variational inference, and hierarchical variants, evaluated on ASSISTments and intervention datasets.
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EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
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EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.