Imagen de Portada

A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shi, Yibo, Lakshminarayanan, Braghadeesh, Rojas, Cristian R.
Formato:	Preprint
Publicado:	2025
Materias:	Systems and Control Methodology Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation
por: Lakshminarayanan, Braghadeesh, et al.
Publicado: (2022)

Fine Tuning a Simulation-Driven Estimator
por: Lakshminarayanan, Braghadeesh, et al.
Publicado: (2025)

On Asymptotic Analysis of the Two-Stage Approach: Towards Data-Driven Parameter Estimation
por: Lakshminarayanan, Braghadeesh, et al.
Publicado: (2025)

Data-Driven Estimation of Structured Singular Values
por: Guerrero, Margarita A., et al.
Publicado: (2025)

Sample Complexity of the Sign-Perturbed Sums Method
por: Szentpéteri, Szabolcs, et al.
Publicado: (2024)

A Cyber Manufacturing IoT System for Adaptive Machine Learning Model Deployment by Interactive Causality Enabled Self-Labeling
por: Ren, Yutian, et al.
Publicado: (2024)

Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
por: Abdalmoaty, Mohamed, et al.
Publicado: (2024)

Finite Sample MIMO System Identification with Multisine Excitation: Nonparametric, Direct, and Two-step Parametric Estimators
por: González, Rodrigo A., et al.
Publicado: (2025)

Assessing Markov Property in Driving Behaviors: Insights from Statistical Tests
por: Li, Zheng, et al.
Publicado: (2025)

Scenario Approach with Post-Design Certification of User-Specified Properties
por: Carè, Algo, et al.
Publicado: (2026)

Deep Adaptive Model-Based Design of Experiments
por: Strouwen, Arno, et al.
Publicado: (2026)

Graph neural networks for power grid operational risk assessment under evolving grid topology
por: Zhang, Yadong, et al.
Publicado: (2024)

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
por: Garcia, Joaquim Dias, et al.
Publicado: (2021)

Metropolis Sampling
por: Martino, Luca, et al.
Publicado: (2017)

Balancing Application Relevant and Sparsity Revealing Excitation in Input Design
por: Parsa, Javad, et al.
Publicado: (2024)

Second Order Ensemble Langevin Method for Sampling and Inverse Problems
por: Liu, Ziming, et al.
Publicado: (2022)

Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems
por: Guerrero, Margarita A., et al.
Publicado: (2025)

Set Invariance with Probability One for Controlled Diffusion: Score-based Approach
por: Wang, Wenqing, et al.
Publicado: (2025)

Backpropagation-Free Metropolis-Adjusted Langevin Algorithm
por: Cobb, Adam D., et al.
Publicado: (2025)

Causal discovery in deterministic discrete LTI-DAE systems
por: Konkathi, Bala Rajesh, et al.
Publicado: (2025)

Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures
por: Noorani, Erfaun, et al.
Publicado: (2025)

On Large-Scale Multiple Testing Over Networks: An Asymptotic Approach
por: Pournaderi, Mehrdad, et al.
Publicado: (2022)

Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
por: Lau, Tim Tsz-Kit, et al.
Publicado: (2023)

The Stochastic Proximal Distance Algorithm
por: Jiang, Haoyu, et al.
Publicado: (2022)

Approximate Linear Programming for Decentralized Policy Iteration in Cooperative Multi-agent Markov Decision Processes
por: Mandal, Lakshmi, et al.
Publicado: (2023)

Data-Driven Sequential Sampling for Tail Risk Mitigation
por: Ahn, Dohyun, et al.
Publicado: (2025)

PID-controlled Langevin Dynamics for Faster Sampling of Generative Models
por: Chen, Hongyi, et al.
Publicado: (2025)

A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System
por: Lin, Zong-Zhi, et al.
Publicado: (2022)

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
por: Besbes, Omar, et al.
Publicado: (2023)

Metropolis-adjusted Subdifferential Langevin Algorithm
por: Ning, Ning
Publicado: (2025)

Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process
por: Miller, Kevin S., et al.
Publicado: (2024)

Modelos Empiricos de Pos-Dupla Selecao por LASSO: Discussoes para Estudos do Transporte Aereo
por: Oliveira, Alessandro V. M.
Publicado: (2025)

From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
por: Anadria, Daniel, et al.
Publicado: (2025)

An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
por: Rajendran, Goutham, et al.
Publicado: (2023)

An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling
por: Huang, Xunpeng, et al.
Publicado: (2024)

Kinetic Langevin Splitting Schemes for Constrained Sampling
por: Chada, Neil K., et al.
Publicado: (2026)

Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
por: Boveiri, Mohammad, et al.
Publicado: (2024)

Stereographic Multiple-Try Metropolis
por: Wang, Zhihao, et al.
Publicado: (2025)

Safety and optimality in learning-based control at low computational cost
por: Baumann, Dominik, et al.
Publicado: (2025)

Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions
por: Pillai, Natesh S.
Publicado: (2022)