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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 751–775 of 2226 papers

TitleStatusHype
Adversarial robustness of amortized Bayesian inferenceCode0
Simultaneous identification of models and parameters of scientific simulatorsCode0
Bayesian calibration of differentiable agent-based models—0
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic LikelihoodCode0
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
A Simple Generative Model of Logical Reasoning and Statistical Learning—0
Massively Parallel Reweighted Wake-Sleep—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
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
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Benchmark Results

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