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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
Bayesian Inference of Regular Expressions from Human-Generated Example Strings—0
Bayesian posterior approximation via greedy particle optimization—0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Nonparametric Bayesian Deep Networks with Local CompetitionCode0
Approximate Bayesian inference in spatial environments—0
Bayesian Joint Spike-and-Slab Graphical LassoCode0
Wasserstein Measure Coresets—0
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive FlowsCode1
Spatial Uncertainty Sampling for End-to-End Control—0
Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo—0
Subsampling Sequential Monte Carlo for Static Bayesian Models—0
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in BadlandsCode0
A Channel-based Exact Inference Algorithm for Bayesian Networks—0
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems—0
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning—0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Mean Field Network based Graph Refinement with application to Airway Tree Extraction—0
A Compressed Sensing Approach for Distribution Matching—0
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference—0
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models—0
Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information—0
Locally Private Bayesian Inference for Count ModelsCode0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels—0
A Probabilistic Disease Progression Model for Predicting Future Clinical OutcomeCode0
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Benchmark Results

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