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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 301–325 of 2226 papers

TitleStatusHype
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning—0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
Bayesian Flow Networks in Continual Learning—0
A Deep Learning Approach to Dst Index Prediction—0
A normative theory of social conflict—0
A Bayesian/Information Theoretic Model of Bias Learning—0
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition—0
Bayesian geoacoustic inversion using mixture density network—0
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
A Bayesian Inference Framework for Procedural Material Parameter Estimation—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
An Interpretable Neural Network for Parameter Inference—0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
A Neural Implementation of the Kalman Filter—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
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Benchmark Results

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