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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 901–950 of 2226 papers

TitleStatusHype
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
Batch Bayesian Optimization via Particle Gradient FlowsCode0
Implicit Full Waveform Inversion with Deep Neural Representation—0
Non-Gaussian Process Regression—0
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution—0
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
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—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
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
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
Robust Bayesian Nonnegative Matrix Factorization with Implicit Regularizers—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
Deep Maxout Network Gaussian Process—0
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy—0
Optimal Rates for Regularized Conditional Mean Embedding Learning—0
Enhanced gradient-based MCMC in discrete spaces—0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference PerspectiveCode0
A Two-stage Multiband WiFi Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference—0
Minimum Description Length Control—0
Mean-field Variational Inference via Wasserstein Gradient Flow—0
Neural Posterior Estimation with Differentiable Simulators—0
Latent Variable Models for Bayesian Causal Discovery—0
Comparative Study of Inference Methods for Interpolative Decomposition—0
Towards Unifying Perceptual Reasoning and Logical Reasoning—0
Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation—0
Variational Bayesian inference for CP tensor completion with side information—0
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport—0
Cold Posteriors through PAC-Bayes—0
Multiband Delay Estimation for Localization Using a Two-Stage Global Estimation Scheme—0
Robust One Round Federated Learning with Predictive Space Bayesian InferenceCode0
Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk—0
Scaling multi-species occupancy models to large citizen science datasets—0
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift—0
Fidelity of Hyperbolic Space for Bayesian Phylogenetic InferenceCode0
Generalised Bayesian Inference for Discrete Intractable LikelihoodCode0
How Adults Understand What Young Children Say—0
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
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Benchmark Results

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