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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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
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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 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.
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.
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.
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.
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.
citing papers explorer
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Expected Free Energy-based Planning as Variational Inference
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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What Type of Inference is Active Inference?
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.
-
Multifrequency Synthesis via CHIBI: Colorful Hierarchical Interferometric Bayesian Imaging
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*.
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High-Dimensional Bayesian Calibration of Expensive Nuclear Models with Differentiable Emulation
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.
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People readily follow personal advice from AI but it does not improve their well-being
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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Differentiable Fuzzy Cosmic-Web for Field Level Inference
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.
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Bayesian Doppler Imaging: Simultaneous Inference of Surface Maps and Geometric Parameters
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.
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Towards Practical Field-Level Inference for Weak Lensing
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.
-
Calibration of an Analog-to-Digital Conversion Nonlinearity in JWST/NIRISS
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.
-
A Strongly Parametrized Mass Ratio Model for the Stable Mass Transfer Channel: a Case Study of the $10 \, \rm{M}_{\odot}$ Peak
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.
-
A unified harmonic framework for dark siren cosmology
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.
-
Stochastic gravitational-wave background search using data from five pulsar timing arrays
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.
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Sifting for a Stream: The Morphology of the $300S$ Stellar Stream
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.
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DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars
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: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
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.
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A renormalization-group inspired lattice-based framework for piecewise generalized linear models
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.
-
Tokenised Flow Matching for Hierarchical Simulation Based Inference
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.
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Multimodal Transformer Based Generic Mixture Density Network for Scattering Timescale Estimation of Fast Radio Bursts
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.
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StanBKT: Rethinking Parameter Estimation in Bayesian Knowledge Tracing
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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Gravitational-wave constraints on $H_0$ are robust to (putative) redshift evolution in the binary black hole mass spectrum at current sensitivity
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.
-
Discovery of a compact hierarchical triple main-sequence star system while searching for binary stars with compact objects
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 for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
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.
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Deployable probabilistic programming
Design guidelines and a Go library (Infergo) for deploying probabilistic programming in production systems, with benchmark comparisons.
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QCD-factorization amplitudes from flavour symmetries: beyond the $SU(3)$ symmetric case
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.
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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.
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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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Extended Structure in E+A Galaxies Via Image Stacking
Image stacking of 57 E+A galaxies shows excess extended light relative to comparison samples, consistent with faint tidal features from small-scale interactions.
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Revisiting the Exo-Mercury Candidate GJ 367 b with ESPRESSO and a Self-Consistent Tidal Distortion Model
Revised mass of 0.503 M_Earth and radius of 0.736 R_Earth for GJ 367 b give a density of 6.9 g cm^{-3} and an iron fraction of 50-70% via new tidal and composition modeling.
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