SOTAVerified

Bayesian Inference

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

Papers

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Showing 1301–1350 of 2226 papers

TitleStatusHype
Dimension reduction via score ratio matching—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation—0
Discriminative Relational Topic Models—0
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting—0
Distilling Calibration via Conformalized Credal Inference—0
Distributed Bayesian inference for consistent labeling of tracked objects in non-overlapping camera networks—0
Distributed Bayesian Inference for Large-Scale IoT Systems—0
Distributed Variational Bayesian Algorithms Over Sensor Networks—0
Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity—0
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation—0
Divide, Conquer, Combine Bayesian Decision Tree Sampling—0
Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?—0
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation—0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference—0
Double Robust Bayesian Inference on Average Treatment Effects—0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control—0
Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference—0
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Dynamics on Lie groups with applications to attitude estimation—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Efficient Attack Graph Analysis through Approximate Inference—0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Efficient Bayesian Inference for Nested Simulators—0
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems—0
Efficient Bayesian species tree inference under the multi-species coalescent—0
Efficient Bayesian synthetic likelihood with whitening transformations—0
Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling—0
Efficient Likelihood Bayesian Constrained Local Model—0
Efficient Low-Order Approximation of First-Passage Time Distributions—0
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods—0
Efficient Membership Inference Attacks by Bayesian Neural Network—0
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline—0
Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning—0
EinSteinVI: General and Integrated Stein Variational Inference—0
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Benchmark Results

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