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 551–575 of 2226 papers

TitleStatusHype
Bandit Learning for Diversified Interactive Recommendation—0
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)—0
Active Inference in Robotics and Artificial Agents: Survey and Challenges—0
Causal Inference through a Witness Protection Program—0
Cohort effects in mortality modelling: a Bayesian state-space approach—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Active inference and deep generative modeling for cognitive ultrasound—0
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Amortized Bayesian Inference for Models of Cognition—0
Calibration and Filtering of Exponential L\'evy Option Pricing Models—0
A Variational View on Bootstrap Ensembles as Bayesian Inference—0
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM—0
A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension—0
Variational inference of fractional Brownian motion with linear computational complexity—0
ABC random forests for Bayesian parameter inference—0
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications—0
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution—0
Automatic Variational Inference in Stan—0
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
Burn-in, bias, and the rationality of anchoring—0
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Benchmark Results

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