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 776–800 of 2226 papers

TitleStatusHype
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Better Peer Grading through Bayesian InferenceCode0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact caseCode1
Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference—0
Conjugate Natural Selection—0
Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Graph-based sequential beamformingCode0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
Simulating how animals learn: a new modelling framework applied to the process of optimal foraging—0
Bayesian Floor Field: Transferring people flow predictions across environmentsCode0
Robust Bayesian Nonnegative Matrix Factorization with Implicit Regularizers—0
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
Bayesian Inference with Latent Hamiltonian Neural NetworksCode1
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy—0
Deep Maxout Network Gaussian Process—0
Optimal Rates for Regularized Conditional Mean Embedding Learning—0
Enhanced gradient-based MCMC in discrete spaces—0
Reliable amortized variational inference with physics-based latent distribution correctionCode1
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference PerspectiveCode0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
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Benchmark Results

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