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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 601–650 of 2226 papers

TitleStatusHype
Federated Variational Inference: Towards Improved Personalization and Generalization—0
Explaining Emergent In-Context Learning as Kernel Regression—0
PDE-constrained Gaussian process surrogate modeling with uncertain data locationsCode0
Augmented Message Passing Stein Variational Gradient Descent—0
Massively Parallel Reweighted Wake-Sleep—0
A Simple Generative Model of Logical Reasoning and Statistical Learning—0
Model-based Validation as Probabilistic InferenceCode0
Score Operator Newton transport—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Object based Bayesian full-waveform inversion for shear elastography—0
The Compositional Structure of Bayesian Inference—0
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulatorsCode1
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches—0
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics—0
Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance—0
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes—0
Inferential Moments of Uncertain Multivariable Systems—0
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss InformationCode0
Variational Inference for Bayesian Neural Networks under Model and Parameter UncertaintyCode0
Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)—0
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems—0
Machine Learning and the Future of Bayesian Computation—0
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferenceCode1
Individual Fairness in Bayesian Neural NetworksCode0
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity—0
Martingale Posterior Neural Processes—0
A Latent Space Theory for Emergent Abilities in Large Language Models—0
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping—0
Playing it safe: information constrains collective betting strategies—0
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse ProblemsCode0
Bayesian Inference on Brain-Computer Interfaces via GLASSCode0
Bayesian Inference for Jump-Diffusion Approximations of Biochemical Reaction Networks—0
Meta-Learned Models of CognitionCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry—0
Cascaded Calibration of Mechatronic Systems via Bayesian Inference—0
Robust Outlier Rejection for 3D Registration with Variational BayesCode1
Bayesian neural networks via MCMC: a Python-based tutorialCode1
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas—0
Leveraging joint sparsity in hierarchical Bayesian learningCode0
Training Language Models with Language Feedback at ScaleCode1
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task—0
Particle Mean Field Variational BayesCode0
Random-effects substitution models for phylogenetics via scalable gradient approximationsCode0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference—0
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS—0
Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified