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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 1776–1800 of 2226 papers

TitleStatusHype
Constrained Sampling with Primal-Dual Langevin Monte CarloCode0
Variational Inference with Mixtures of Isotropic GaussiansCode0
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate LikelihoodsCode0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
Distilling Importance Sampling for Likelihood Free InferenceCode0
Distilling Model KnowledgeCode0
Conditional Optimal Transport on Function SpacesCode0
Probing as Quantifying Inductive BiasCode0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Uncertainty in latent representations of variational autoencoders optimized for visual tasksCode0
Bayesian Neural Networks at Finite TemperatureCode0
Locally Private Bayesian Inference for Count ModelsCode0
Sparse online variational Bayesian regressionCode0
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamicsCode0
Conditionally Independent Multiresolution Gaussian ProcessesCode0
Logistic Variational Bayes RevisitedCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High DimensionsCode0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Projected Wasserstein gradient descent for high-dimensional Bayesian inferenceCode0
Conditional diffusions for amortized neural posterior estimationCode0
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive ModelsCode0
Uncertainty in Neural Networks: Approximately Bayesian EnsemblingCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
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Benchmark Results

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