Tootfinder

Bayesian Parameter Inference and Uncertainty-Informed Sensitivity Analysis in a 0D Cardiovascular Model for Intraoperative Hypotension
Jan-Niklas Thiel, Marko Zlicar, Ulrich Steinseifer, Borut Kirn, Michael Neidlin
https://arxiv.org/abs/2509.14354

Bayesian Parameter Inference and Uncertainty-Informed Sensitivity Analysis in a 0D Cardiovascular Model for Intraoperative Hypotension
Computational cardiovascular models are promising tools for clinical decision support, particularly in complex conditions, such as intraoperative hypotension (IOH). IOH arises from different mechanisms, making treatment selection non-trivial. Patient-specific predictions require calibration, typically performed using deterministic approaches prone to parameter non-identifiability and lacking uncertainty quantification, hindering clinical translation. Consequently, Bayesian approaches are needed…

Learning Informed Prior Distributions with Normalizing Flows for Bayesian Analysis
Hendrik Roch, Chun Shen
https://arxiv.org/abs/2509.14911 https://arxiv.o…

Learning Informed Prior Distributions with Normalizing Flows for Bayesian Analysis
We investigate the use of normalizing flow (NF) models as flexible priors in Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling. Trained on posteriors from previous analyses, these models can be used as informative priors, capturing non-trivial distributions and correlations, in subsequent inference tasks. We compare different training strategies and loss functions, finding that training based on Kullback-Leibler (KL) divergence and unsupervised learning consistently yield the mos…

Crosslisted article(s) found for math.NA. https://arxiv.org/list/math.NA/new
[1/1]:
- A Bayesian thinning algorithm for the point source identification of heat equation
Zhiliang Deng, Chen Li, Xiaomei Yang

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
Yuko Kakikawa, Yoshiyuki Ninomiya
https://arxiv.org/abs/2510.11172 https:/…

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
In the field of spatial data analysis, spatially varying coefficients (SVC) models, which allow regression coefficients to vary by region and flexibly capture spatial heterogeneity, have continued to be developed in various directions. Moreover, the Bayesian generalized fused lasso is often used as a method that efficiently provides estimation under the natural assumption that regression coefficients of adjacent regions tend to take the same value. In most Bayesian methods, the selection of pri…

Multi-Physics-Enhanced Bayesian Inverse Analysis: Information Gain from Additional Fields
Lea J. Haeusel, Jonas Nitzler, Lea J. K\"oglmeier, Wolfgang A. Wall
https://arxiv.org/abs/2510.11095

Multi-Physics-Enhanced Bayesian Inverse Analysis: Information Gain from Additional Fields
Many real-world inverse problems suffer from limited data, often because they rely on measurements of a single physical field. Such data frequently fail to sufficiently reduce parameter uncertainty in Bayesian inverse analysis. Incorporating easily available data from additional physical fields can substantially decrease this uncertainty. We focus on Bayesian inverse analyses based on computational models, e.g., those using the finite element method. To incorporate data from additional physical…

A High-Performance Training-Free Pipeline for Robust Random Telegraph Signal Characterization via Adaptive Wavelet-Based Denoising and Bayesian Digitization Methods
Tonghe Bai, Ayush Kapoor, Na Young Kim
https://arxiv.org/abs/2510.10752

A High-Performance Training-Free Pipeline for Robust Random Telegraph Signal Characterization via Adaptive Wavelet-Based Denoising and Bayesian Digitization Methods
Random telegraph signal (RTS) analysis is increasingly important for characterizing meaningful temporal fluctuations in physical, chemical, and biological systems. The simplest RTS arises from discrete stochastic switching events between two binary states, quantified by their transition amplitude and dwell times in each state. Quantitative analysis of RTSs provides valuable insights into microscopic processes such as charge trapping in semiconductors. However, analyzing RTS becomes considerably…

Efficient Prior Sensitivity and Tipping-point Analysis for Medical Research: Revisiting Sampling Importance Resampling
Tomohiro Ohigashi, Shonosuke Sugasawa
https://arxiv.org/abs/2510.10034

Efficient Prior Sensitivity and Tipping-point Analysis for Medical Research: Revisiting Sampling Importance Resampling
Bayesian methods have received increasing attention in medical research, where sensitivity analysis of prior distributions is essential. Such analyses typically require the evaluation of the posterior distribution of a parameter under multiple alternative prior settings. When the posterior distribution of the parameter of interest cannot be derived analytically, the standard approach is to re-fit the Markov chain Monte Carlo (MCMC) algorithm for each setting, which incurs substantial computatio…

Abundance Pattern Fitting with Bayesian Inference: Constraining First Stars' Properties and Their Explosion Mechanism with Extremely Metal-poor Stars
Ruizheng Jiang, Haining Li, Gang Zhao, Qianfan Xing, Wenyu Xin
https://arxiv.org/abs/2510.05712

Abundance Pattern Fitting with Bayesian Inference: Constraining First Stars' Properties and Their Explosion Mechanism with Extremely Metal-poor Stars
The abundance patterns of extremely metal-poor stars preserve a fossil record of the Universe's earliest chemical enrichment by the supernova explosions from the evolution of first generation of stars, also referred to as Population III (or Pop III). By applying Bayesian inference to the analysis of abundance patterns of these ancient stars, this study presents a systematic investigation into the properties and explosion mechanism of Pop III stars. We apply NLTE corrections to enhance the relia…

Multi-objective Bayesian optimization for blocking in extreme value analysis and its application in additive manufacturing
Shehzaib Irfan, Nabeel Ahmad, Alexander Vinel, Daniel F. Silva, Shuai Shao, Nima Shamsaei, Jia Liu
https://arxiv.org/abs/2510.11960

Multi-objective Bayesian optimization for blocking in extreme value analysis and its application in additive manufacturing
Extreme value theory (EVT) is well suited to model extreme events, such as floods, heatwaves, or mechanical failures, which is required for reliability assessment of systems across multiple domains for risk management and loss prevention. The block maxima (BM) method, a particular approach within EVT, starts by dividing the historical observations into blocks. Then the sample of the maxima for each block can be shown, under some assumptions, to converge to a known class of distributions, which …

Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
Sophia V. Kuhn, Rafael Bischof, Marius Weber, Antoine Binggeli, Michael A. Kraus, Walter Kaufmann, Fernando P\'erez-Cruz
https://arxiv.org/abs/2509.25031

Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
Aging infrastructure portfolios pose a critical resource allocation challenge: deciding which structures require intervention and which can safely remain in service. Structural assessments must balance the trade-off between cheaper, conservative analysis methods and accurate but costly simulations that do not scale portfolio-wide. We propose Bayesian neural network (BNN) surrogates for rapid structural pre-assessment of worldwide common bridge types, such as reinforced concrete frame bridges. T…

Unveiling Dynamics and Variability in Open Clusters: Insights from a Comprehensive Analysis of Six Galactic Clusters
Kuldeep Belwal, D. Bisht, Ing-Guey Jiang, R. K. S. Yadav, Ashish Raj, Geeta Rangwal, Arvind K. Dattatrey, Mohit Singh Bisht, Alok Durgapal
https://arxiv.org/abs/2510.02462

Unveiling Dynamics and Variability in Open Clusters: Insights from a Comprehensive Analysis of Six Galactic Clusters
We present a kinematic and dynamical analysis of six Galactic open clusters NGC~2204, NGC~2660, NGC~2262, Czernik~32, Pismis~18, and NGC~2437, using \textit{Gaia}~DR3. We used Bayesian and Gaussian Mixture Model (GMM) methods to identify cluster members, but chose GMM because it's more appropriate for low-mass stars. Estimated distances range from 1.76 to 4.20~kpc and ages from 0.199 to 1.95~Gyr, confirming their intermediate-age nature. King model fits indicate compact morphologies, with core …

PriorWeaver: Prior Elicitation via Iterative Dataset Construction
Yuwei Xiao, Shuai Ma, Antti Oulasvirta, Eunice Jun
https://arxiv.org/abs/2510.06550 https://

PriorWeaver: Prior Elicitation via Iterative Dataset Construction
In Bayesian analysis, prior elicitation, or the process of explicating one's beliefs to inform statistical modeling, is an essential yet challenging step. Analysts often have beliefs about real-world variables and their relationships. However, existing tools require analysts to translate these beliefs and express them indirectly as probability distributions over model parameters. We present PriorWeaver, an interactive visualization system that facilitates prior elicitation through iterative dat…

Bayesian Re-Analysis of the Phylogenetic Topology of Early SARS-CoV-2 Case Sequences
Michael B. Weissman
https://arxiv.org/abs/2510.01484 https://arxiv.org…

Bayesian Re-Analysis of the Phylogenetic Topology of Early SARS-CoV-2 Case Sequences
A much-cited 2022 paper by Pekar et al. claimed that Bayesian analysis of the molecular phylogeny of early SARS-CoV-2 cases indicated that it was more likely that two successful introductions to humans had occurred than that just one had. Here I show that after correcting a fundamental error in Bayesian reasoning the results in that paper give larger likelihood for a single introduction than for two.

Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integration
Christina Schenk, Jacobo Ayensa Jim\'enez, Ignacio Romero
https://arxiv.org/abs/2509.18998

A Simulation Study on the Cosmic Ray Energy Spectra of Elemental Mass Groups using the Tibet Air Shower and Muon Detector Arrays through the Bayesian Unfolding Method
G. Imaizumi, M. Anzorena, K. Fujita, Y. Katayose, S. Kato, T. Kawashima, K. Kawata, A. Mizuno, M. Ohnishi, R. Garcia, T. Sako, F. Sugimoto, M. Takita, Y. Yokoe
https://arxiv.…

A Simulation Study on the Cosmic Ray Energy Spectra of Elemental Mass Groups using the Tibet Air Shower and Muon Detector Arrays through the Bayesian Unfolding Method
We study the analysis method to determine the cosmic-ray energy spectra of different mass groups assuming the use of the Tibet AS$γ$ experiment, which consists of the high-density Tibet air-shower array (Tibet-AS) and the underground muon detector (MD) array. These arrays measure the sampling air shower size $Σρ$ and the total muon number $ΣN_μ$ of each air shower event. These parameters are known to contain information on the energy and mass of the primary particle. To reconstruct the ene…

When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He
https://arxiv.org/abs/2509.24912 https://

When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
Score-based methods, such as diffusion models and Bayesian inverse problems, are often interpreted as learning the data distribution in the low-noise limit ($σ\to 0$). In this work, we propose an alternative perspective: their success arises from implicitly learning the data manifold rather than the full distribution. Our claim is based on a novel analysis of scores in the small-$σ$ regime that reveals a sharp separation of scales: information about the data manifold is $Θ(σ^{-2})$ stronger…

Photometric Analysis of 30 Contact Binaries in M31
Xiang Gao, Kai Li, Li-Heng Wang, Hai-bo Yuan, Hong-rui Gu, Ya-Ni Guo
https://arxiv.org/abs/2510.02993 https://

Photometric Analysis of 30 Contact Binaries in M31
M31, as the largest galaxy in the Local Group, is of significant importance for the study of stellar formation and evolution. Based on the data of 5,859 targets observed in M31 by Gu et al (\citeyear{2024ApJS..273....9G}), we selected 30 contact binaries by visual inspection for further study. Using the PHOEBE software and employing Bayesian optimization and Markov Chain Monte Carlo sampling, we determined the physical parameters of these 30 systems. The results show that 10 systems exhibit the…

Amortized Bayesian Inference for Spatio-Temporal Extremes: A Copula Factor Model with Autoregression
Carlos A. Pasquier, Luis A. Barboza
https://arxiv.org/abs/2510.02618 https:/…

Amortized Bayesian Inference for Spatio-Temporal Extremes: A Copula Factor Model with Autoregression
We develop a Bayesian spatio-temporal framework for extreme-value analysis that augments a hierarchical copula model with an autoregressive factor to capture residual temporal dependence in threshold exceedances. The factor can be specified as spatially varying or spatially constant, and the scale parameter incorporates scientifically relevant covariates (e.g., longitude, latitude, altitude), enabling flexible representation of geographic heterogeneity. To avoid the computational burden of the …

Crosslisted article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- Bayesian Re-Analysis of the Phylogenetic Topology of Early SARS-CoV-2 Case Sequences
Michael B. Weissman

Adaptive Reconstruction of Cluster Halos (ARCH): Integrating Shear and Flexion for Substructure Detection
Jacob Shpiece, David M. Goldberg
https://arxiv.org/abs/2509.21686 https…

Adaptive Reconstruction of Cluster Halos (ARCH): Integrating Shear and Flexion for Substructure Detection
We present ARCH (Adaptive Reconstruction of Cluster Halos), a new gravitational lensing pipeline for cluster mass reconstruction that applies a joint shear-flexion analysis to JWST imaging. Previous approaches have explored joint shear+flexion reconstructions through forward modeling and Bayesian inference frameworks; in contrast, ARCH adopts a staged optimization strategy that incrementally filters and selects candidate halos rather than requiring a global likelihood model or strong priors. Th…

Bayesian power spectral density estimation for LISA noise based on P-splines with a parametric boost
Nazeela Aimen, Patricio Maturana-Russel, Avi Vajpeyi, Nelson Christensen, Renate Meyer
https://arxiv.org/abs/2510.00533

Bayesian power spectral density estimation for LISA noise based on P-splines with a parametric boost
Flexible and efficient noise characterization is crucial for the precise estimation of gravitational wave parameters. We introduce a fast and accurate Bayesian method for estimating the power spectral density (PSD) of long, stationary time series tailored specifically for LISA data analysis. Our approach models the PSD as a geometric mean of a parametric and a nonparametric component, combining the computational efficiency of parametric models with the flexibility to capture deviations from the…

Bayesian Framework for the E1 and E2 Astrophysical Factors at 300 keV from Subthreshold and Ground-State Asymptotic Normalization Coefficients
A. M. Mukhamedzhanov
https://arxiv.org/abs/2509.17102

Bayesian Framework for the E1 and E2 Astrophysical Factors at 300 keV from Subthreshold and Ground-State Asymptotic Normalization Coefficients
The $^{12}\mathrm{C}(α,γ)^{16}\mathrm{O}$ reaction governs the carbon-to-oxygen ratio set during helium burning, shaping white-dwarf structure and Type~Ia supernova yields. At the astrophysical energy $E \approx 300~\mathrm{keV}$, the cross section is controlled by the subthreshold $1^{-}$ (7.12~MeV) and $2^{+}$ (6.92~MeV) states, whose contributions depend on their asymptotic normalization coefficients (ANCs) $C_{1}$ and $C_{2}$, respectively. We perform a Bayesian analysis of the $S_{E1}(30…

Crosslisted article(s) found for math.NA. https://arxiv.org/list/math.NA/new
[1/1]:
- A Bayesian Characterization of Ensemble Kalman Updates
Frederic J. N. Jorgensen, Youssef M. Marzouk

A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data
Joaquim Valerio Teixeira, Ed Reznik, Sudpito Banerjee, Wesley Tansey
https://arxiv.org/abs/2509.20636

A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data
The analysis of spatial data from biological imaging technology, such as imaging mass spectrometry (IMS) or imaging mass cytometry (IMC), is challenging because of a competitive sampling process which convolves signals from molecules in a single pixel. To address this, we develop a scalable Bayesian framework that leverages natural sparsity in spatial signal patterns to recover relative rates for each molecule across the entire image. Our method relies on the use of a heavy-tailed variant of th…

Cosmic Multipoles in Galaxy Surveys II: Comparing Different Methods in Assessing the Cosmic Dipole
Vasudev Mittal, Oliver T. Oayda, Geraint F. Lewis
https://arxiv.org/abs/2509.20651

Cosmic Multipoles in Galaxy Surveys II: Comparing Different Methods in Assessing the Cosmic Dipole
We present a comparative analysis of estimators and Bayesian methods for determining the number count dipole from cosmological surveys. The increase in discordance between the number count dipole and the CMB's kinematic dipole has presented a challenge to the assumption of an isotropic and homogeneous universe. The level of discordance has depended on the choice of method to determine the dipole; hence, there is a need to compare them to determine the better approach. We select the healpy fit_d…

Cosmic dipole tensions: confronting the Cosmic Microwave Background with infrared and radio populations of cosmological sources
Mali Land-Strykowski, Geraint F. Lewis, Tara Murphy
https://arxiv.org/abs/2509.18689

Bayesian Nonhomogeneous hidden Markov models to leverage routine in physical activity monitoring with informative wear time
Beatrice Cantoni, Savannah V. Rauschendorfer, Michael E. Roth, J. Andrew Livingston, Eugenie S. Kleinerman, Corwin M. Zigler
https://arxiv.org/abs/2509.17806

Bayesian Nonhomogeneous hidden Markov models to leverage routine in physical activity monitoring with informative wear time
Missing data is among the most prominent challenges in the analysis of physical activity (PA) data collected from wearable devices, with the threat of nonignorabile missingness arising when patterns of device wear relate to underlying activity patterns. We offer a rigorous consideration of assumptions about missing data mechanisms in the context of the common modeling paradigm of state space models with a finite, meaningful, set of underlying PA states. Focusing in particular on hidden Markov m…

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Sparse Bayesian joint modal estimation for exploratory item factor analysis
Keiichiro Hijikata, Motonori Oka, Kensuke Okada