#bayesian inference
33 resultsMaximum Likelihood and Bayesian Estimation for State-Space Models Using the Non-Gaussian Filter
Genshiro Kitagawa
The paper revisits deterministic non‑Gaussian filtering for nonlinear state‑space models, showing it can be used effectively for maximum likelihood and Bayesian estimation thanks t…
Bayesian Inference of Discretization Error Means in ODEs via Ensemble Kalman Filtering
Shoji Toyota, Yuto Miyatake
The paper introduces a Bayesian method that uses an Ensemble Kalman Filter to estimate the mean of discretization errors in ODE solvers, employing a Markov prior that reflects erro…
Bayesian Graphical Models under Positivity Constraints: A Scalable generalized likelihood Approach
Swarnali Raha, Partha Sarkar, Sirani Perera +1
The paper proposes a scalable Bayesian method for estimating precision matrices in Gaussian graphical models with total positivity constraints, using a D‑trace loss and spike‑and‑s…
Impact of numerical stability in Bayesian noise wave calibration on global 21-cm experiments
Saswata Dasgupta, Adarsh Kumar Dash, Dominic Anstey +3
The paper identifies and mitigates a numerical instability in the Bayesian noise‑wave calibration used for global 21‑cm experiments, improving reproducibility and accuracy of recei…
Bayesian Plackett--Luce latent block models for ranked data
Lapo Santi, Nial Friel, Valeria Vitelli
The paper proposes a Bayesian latent block model that jointly clusters assessors and items for ranked data using a Plackett–Luce observation model, with inference via Gibbs samplin…
Dynamic output-feedback stabilization of uncertain linear dynamics via digital twins
Philipp A. Guth, Karl Kunisch, Sergio S. Rodrigues +1
The paper proposes a digital‑twin framework that runs alongside an uncertain linear system, using real‑time data to estimate the system state and parameters while generating a stab…
BayesAME: Bayesian Active Model Evaluation
Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet +2
BayesAME is a Bayesian sequential framework that automatically determines the size of a coreset for evaluating large generative models, using latent ability models and information‑…
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
Peter Tisnikar, Maja Swieczkowska, Benteng Ma +2
The paper proposes a Bayesian method (CE-CM) for estimating hidden partner capabilities in multi‑task ad‑hoc teamwork, allowing agents to plan with decentralized execution and adap…
QuickGWecc: Fast Bayesian pipeline for searching eccentric binaries in pulsar timing array data
Lankeswar Dey, Bence Bécsy, Abhimanyu Susobhanan +1
The paper introduces QuickGWecc, a fast Bayesian pipeline that extends the QuickCW framework to efficiently search for continuous gravitational waves from eccentric supermassive bl…
ForMoSA: Forward Modeling tool for Spectral Analysis
ForMoSA Collaboration, Simon Petrus, Paulina Palma-Bifani +9
ForMoSA is an open‑source Python package that uses Bayesian methods to fit spectroscopic and photometric data of directly imaged young planetary‑mass brown dwarfs and exoplanets, s…
Reclaiming the "frequentist" role of marginal likelihood in Bayesian belief revision
Abdelhakim Aknouche
The paper argues that the marginal likelihood in Bayesian updating should be treated as an active regularizer rather than a static constant, showing its role in governing the long‑…
Three Failures of Pain Location: Why the Diagnostic Utility of Symptom Localization Is Not One Thing
Adam Y Shavit
The paper identifies three distinct ways that patient-reported pain location can fail to aid diagnosis and proposes a spatial Bayesian model that separates reported location from f…
Adaptive Score-Based VAMP: Self-Tuning Hyperparameters via Tilted EM
Siqi Na, Tadashi Wadayama
The paper proposes an adaptive version of score‑based vector approximate message passing (SC‑VAMP) that automatically tunes hyperparameters using a local tilted EM step, achieving…
Higher-Order Hit-&-Run Samplers for Linearly Constrained Densities
Richard D. Paul, Anton Stratmann, Johann F. Jadebeck +4
The paper introduces a new MCMC algorithm that combines higher‑order information (gradients and curvature of the log‑density) with Hit‑and‑Run proposals to efficiently sample distr…
Operator-Split Bayesian Learning for Elliptic PDEs with Unequal Interior and Boundary Data
Emmanuel E. Oguadimma
The paper introduces an operator-split Bayesian learning framework that uses independent Bayesian neural‑network priors for interior source and boundary data to solve second‑order…
Joint Estimation of Properties of the Lunar Subsurface and Galactic Foregrounds with LuSEE-Night
Fatima Yousuf, Zack Li, Stuart D. Bale +36
The paper presents a Bayesian method to simultaneously estimate the dielectric properties of the lunar subsurface at the LuSEE‑Night landing site and the parameters of the galactic…
IMMNet: Hybrid Fusion of Model-based and Data-driven Approaches for Maneuvering Target Tracking
Yixuan Zhao, Chaoqun Yang, Lin Gao +2
The paper introduces IMMNet, a hybrid algorithm that combines the interacting multiple model (IMM) Bayesian framework with learnable neural network components to improve three‑dime…
Mixture of Directed Graphical Models for Discrete Spatial Random Fields
J. Brandon Carter, Catherine A. Calder
The paper introduces a mixture of directed graphical models (MDGMs) as a computationally efficient Bayesian alternative to traditional Markov random field approaches for modeling d…
Bayesian Inference for Extracting Barrier Distributions from Fusion Excitation Functions
Aaron Philip, Pablo Giuliani, Kyle Godbey
The paper presents a Bayesian machine‑learning approach (AutoBNN) for extracting nuclear barrier distributions from sparse fusion excitation function data, providing calibrated unc…
Fundamental Recovery Bounds for SPAD Signals under Stationary Flux
Lior Dvir, Nadav Torem, Mohit Gupta +1
The paper derives likelihood score functions for three SPAD sensing modes, establishes fundamental Cramér‑Rao recovery limits (including Bayesian extensions), and proposes diffusio…
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee
The paper examines how neural network architectures such as feedforward nets, Deep Sets, and Transformers can be used to amortize Bayesian inference, providing fast approximate pos…
Decision Making Needs Uncertainty Quantification [Lecture Notes]
Osvaldo Simeone
The note explains how representing uncertainty appropriately is essential for optimal decision making, showing that risk‑neutral agents need full posterior distributions while risk…
Peak-Decomposition-Free Inverse Metrology of Hyperspectral Moiré Photoluminescence
Katsunori Wakabayashi
The paper introduces a peak‑decomposition‑free inverse framework that uses descriptor maps derived from hyperspectral photoluminescence data of moiré transition‑metal dichalcogenid…
Optimal photostimulation selection for iterative activity maps
Jacob J. Morra, Kaitlyn E. Fouke, Owen Traubert +1
The paper introduces OPhELIA, a Bayesian framework that selects informative optogenetic stimulation patterns to efficiently map neural connectivity with limited experimental trials…