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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 51–100 of 2226 papers

TitleStatusHype
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching—0
Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance SpectroscopyCode0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
Ensemble Kalman filter for uncertainty in human language comprehension—0
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties—0
Bayesian Robust Aggregation for Federated LearningCode0
Data-driven Approach for Interpolation of Sparse DataCode0
Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges—0
Array Partitioning Based Near-Field Attitude and Location Estimation—0
Bayesian Sensing for Time-Varying Channels in ISAC Systems—0
Segmentation with Noisy Labels via Spatially Correlated DistributionsCode0
Position: Bayesian Statistics Facilitates Stakeholder Participation in Evaluation of Generative AI—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—0
Adaptive modelling of anti-tau treatments for neurodegenerative disorders based on the Bayesian approach with physics-informed neural networks—0
Bayesian continual learning and forgetting in neural networksCode1
Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events—0
Robust and Scalable Variational BayesCode0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations—0
Power-scaled Bayesian Inference with Score-based Generative Models—0
Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biologyCode0
Uncertainty Propagation in the Fast Fourier TransformCode0
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference—0
Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach—0
Programs as Singularities—0
Stacking Variational Bayesian Monte Carlo—0
The challenge of uncertainty quantification of large language models in medicine—0
Probabilistic Position-Aided Beam Selection for mmWave MIMO Systems—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers—0
Multi-fidelity Parameter Estimation Using Conditional Diffusion Models—0
Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference—0
HomeEmergency -- Using Audio to Find and Respond to Emergencies in the Home—0
Bayesian Predictive Coding—0
Accelerated Stein Variational Gradient FlowCode0
A friendly introduction to triangular transportCode1
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
E-PINNs: Epistemic Physics-Informed Neural Networks—0
AutoBayes: A Compositional Framework for Generalized Variational Inference—0
Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models—0
SuperARC: An Agnostic Test for Narrow, General, and Super Intelligence Based On the Principles of Recursive Compression and Algorithmic ProbabilityCode0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble—0
Simulation-based Bayesian inference under model misspecification—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural NetworksCode0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
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Benchmark Results

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