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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 101–125 of 2226 papers

TitleStatusHype
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU NetworksCode1
BayesDLL: Bayesian Deep Learning LibraryCode1
A Bayesian algorithm for retrosynthesisCode1
BayesFlow: Learning complex stochastic models with invertible neural networksCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
Fully Adaptive Bayesian Algorithm for Data Analysis, FABADACode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Gaussian process learning of nonlinear dynamicsCode1
Inferring COVID-19 spreading rates and potential change points for case number forecastsCode1
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte CarloCode1
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy imagesCode1
BayesianFitForecast: A User-Friendly R Toolbox for Parameter Estimation and Forecasting with Ordinary Differential EquationsCode1
Lifelong Incremental Reinforcement Learning with Online Bayesian InferenceCode1
Listening to the Noise: Blind Denoising with Gibbs DiffusionCode1
Bayesian Coresets: Revisiting the Nonconvex Optimization PerspectiveCode1
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
Bayesian Uncertainty for Gradient Aggregation in Multi-Task LearningCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variableCode1
A friendly introduction to triangular transportCode1
Bayesian graph convolutional neural networks via tempered MCMCCode1
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in GermanyCode1
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Multi-marginal optimal transport and probabilistic graphical modelsCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
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Benchmark Results

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