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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 1826–1850 of 2226 papers

TitleStatusHype
q-Paths: Generalizing the Geometric Annealing Path using Power MeansCode0
A Bayesian Perspective on the Deep Image PriorCode0
Uncertainty Propagation in the Fast Fourier TransformCode0
The Reciprocal Bayesian LASSOCode0
Quantifying tissue growth, shape and collision via continuum models and Bayesian inferenceCode0
Efficient Bayesian Inference of Sigmoidal Gaussian Cox ProcessesCode0
Adversarial α-divergence Minimization for Bayesian Approximate InferenceCode0
Quantile Propagation for Wasserstein-Approximate Gaussian ProcessesCode0
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceCode0
Stein Point Markov Chain Monte CarloCode0
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in NeuroimagingCode0
Efficient identification of informative features in simulation-based inferenceCode0
Variationally Inferred Sampling Through a Refined Bound for Probabilistic ProgramsCode0
matLeap: A fast adaptive Matlab-ready tau-leaping implementation suitable for Bayesian inferenceCode0
Spike Sorting using the Neural Clustering ProcessCode0
Variationally Inferred Sampling Through a Refined BoundCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian InferenceCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
A Metalearned Neural Circuit for Nonparametric Bayesian InferenceCode0
The Two Kinds of Free Energy and the Bayesian RevolutionCode0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution SettingsCode0
Random-effects substitution models for phylogenetics via scalable gradient approximationsCode0
Random Feature Stein DiscrepanciesCode0
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Benchmark Results

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