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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 801–850 of 2226 papers

TitleStatusHype
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
Scalable Bayesian Inference for Detection and Deblending in Astronomical ImagesCode1
Neural Posterior Estimation with Differentiable Simulators—0
Latent Variable Models for Bayesian Causal Discovery—0
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
Laplacian Autoencoders for Learning Stochastic RepresentationsCode1
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
Generalised Bayesian Inference for Discrete Intractable LikelihoodCode0
Fidelity of Hyperbolic Space for Bayesian Phylogenetic InferenceCode0
Personalized Federated Learning via Variational Bayesian InferenceCode1
How Adults Understand What Young Children Say—0
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations—0
PAVI: Plate-Amortized Variational Inference—0
On the safe use of prior densities for Bayesian model selection—0
Scalable Deep Gaussian Markov Random Fields for General GraphsCode1
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel RecombinationCode1
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees—0
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement—0
Unifying Summary Statistic Selection for Approximate Bayesian ComputationCode0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Information Threshold, Bayesian Inference and Decision-Making—0
Functional Ensemble DistillationCode0
Excess risk analysis for epistemic uncertainty with application to variational inference—0
Bayesian Inference of Stochastic Dynamical Networks—0
Bayesian Inference for the Multinomial Probit Model under Gaussian Prior Distribution—0
Variational inference via Wasserstein gradient flowsCode1
Robust Anytime Learning of Markov Decision ProcessesCode0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate LikelihoodsCode0
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex OptimizationCode0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLCode0
Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density EstimatorsCode1
Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative ModelsCode0
Extended molt phenology models improve inferences about molt duration and timingCode0
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Benchmark Results

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