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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 651–675 of 2226 papers

TitleStatusHype
Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET—0
PyVBMC: Efficient Bayesian inference in PythonCode1
Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference—0
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian InferenceCode0
Simulation-based Bayesian inference for robotic grasping—0
Fast post-process Bayesian inference with Variational Sparse Bayesian QuadratureCode0
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal MetricsCode0
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamicsCode0
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering—0
A normative theory of social conflict—0
Bayesian inference with finitely wide neural networks—0
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics—0
Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme—0
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference—0
Deep Learning Enhanced Realized GARCHCode0
Parameters for > 300 million Gaia stars: Bayesian inference vs. machine learning—0
Event Temporal Relation Extraction with Bayesian Translational Model—0
Trading Information between Latents in Hierarchical Variational AutoencodersCode0
Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis—0
Geometry of Score Based Generative Models—0
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetsCode1
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Fortuna: A Library for Uncertainty Quantification in Deep LearningCode2
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Benchmark Results

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