Large longitudinal RCT finds high rates of following AI personal advice but no sustained well-being gains versus a hobbies control condition.
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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
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.
A differentiable forward model and likelihood enable probabilistic inference over many spatial morphologies for the Galactic Center gamma-ray Excess using variational methods on GPUs.
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.
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.
Conversational AI matches self-directed internet search in increasing belief in true political information and decreasing belief in misinformation.
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.
StanBKT provides a unified Bayesian inference framework for BKT models supporting HMC, variational inference, and hierarchical variants, evaluated on ASSISTments and intervention datasets.
Spectral-siren H0 constraints from GWTC-4.0 binary black holes remain robust when the mass spectrum is permitted to evolve with redshift at current detector sensitivity.
A new compact hierarchical triple main-sequence star system G1010 was discovered through combined low- and high-SNR spectroscopy, Gaia DR3 data, and TESS light curve analysis, showing an inner eclipsing binary rather than a compact object companion.
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.
Design guidelines and a Go library (Infergo) for deploying probabilistic programming in production systems, with benchmark comparisons.
A data-driven SU(3)-breaking analysis of B to PP decays yields QCD-factorization amplitudes that resemble dynamical predictions and require no enhanced annihilation terms.
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.
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