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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 176–200 of 2226 papers

TitleStatusHype
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in GermanyCode1
Monte Carlo guided Diffusion for Bayesian linear inverse problemsCode1
Multi-marginal optimal transport and probabilistic graphical modelsCode1
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
Online Bayesian Goal Inference for Boundedly-Rational Planning AgentsCode1
Bayesian Inference with Latent Hamiltonian Neural NetworksCode1
Out-of-Distribution Detection Using Union of 1-Dimensional SubspacesCode1
Accelerated Bayesian SED Modeling using Amortized Neural Posterior EstimationCode1
Persistent Sampling: Enhancing the Efficiency of Sequential Monte CarloCode1
Physics-Informed Gaussian Process Regression Generalizes Linear PDE SolversCode1
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian InferenceCode1
Understanding and Accelerating Particle-Based Variational InferenceCode1
Bayesian neural networks via MCMC: a Python-based tutorialCode1
Validated Variational Inference via Practical Posterior Error BoundsCode1
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse PlanningCode1
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU NetworksCode1
GAN-based Priors for Quantifying UncertaintyCode1
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection areaCode1
QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative ModelsCode1
Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian NetworksCode1
Reliable amortized variational inference with physics-based latent distribution correctionCode1
Repulsive Deep Ensembles are BayesianCode1
RNN with Particle Flow for Probabilistic Spatio-temporal ForecastingCode1
Bayesian Uncertainty for Gradient Aggregation in Multi-Task LearningCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
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Benchmark Results

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