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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 2001–2025 of 2226 papers

TitleStatusHype
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo—0
Bottom-up data integration in polymer models of chromatin organisation—0
Bridging Privacy and Robustness for Trustworthy Machine Learning—0
Decipherment Complexity in 1:1 Substitution Ciphers—0
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control—0
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Decoupled Variational Gaussian Inference—0
Deep Active Inference for Autonomous Robot Navigation—0
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference—0
Deep de Finetti: Recovering Topic Distributions from Large Language Models—0
Deep Ensemble as a Gaussian Process Approximate Posterior—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Deep Knowledge Tracing with Learning Curves—0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Deep Learning and Bayesian inference for Inverse Problems—0
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI—0
Deep Learning Surrogates for Real-Time Gas Emission Inversion—0
Deep Maxout Network Gaussian Process—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Stable neural networks: large-width asymptotics and convergence rates—0
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Benchmark Results

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