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 1676–1700 of 2226 papers

TitleStatusHype
Surrogate-assisted Bayesian inversion for landscape and basin evolution modelsCode0
Probabilistic Model Checking of Robots Deployed in Extreme Environments—0
Physics-Based Learning for Robotic Environmental Sensing—0
Sampling-based Bayesian Inference with gradient uncertainty—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
That's Mine! Learning Ownership Relations and Norms for Robots—0
Bandit Learning with Implicit FeedbackCode0
Predictive Approximate Bayesian Computation via Saddle Points—0
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments—0
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues—0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences—0
Stochastic Gradient MCMC with Repulsive ForcesCode0
Uncertainty propagation in neural networks for sparse coding—0
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting—0
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference—0
Amortized Bayesian inference for clustering modelsCode0
Surrogate-assisted parallel tempering for Bayesian neural learningCode0
Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching HeteroskedasticityCode0
Black-Box Autoregressive Density Estimation for State-Space Models—0
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis—0
BAR: Bayesian Activity Recognition using variational inference—0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
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Benchmark Results

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