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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 1476–1500 of 2226 papers

TitleStatusHype
Mutually Regressive Point ProcessesCode0
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High DimensionsCode0
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models—0
AGEM: Solving Linear Inverse Problems via Deep Priors and SamplingCode0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Transflow Learning: Repurposing Flow Models Without Retraining—0
Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank TransactionsCode0
Differentially Private Federated Variational InferenceCode0
Measuring Uncertainty through Bayesian Learning of Deep Neural Network StructureCode0
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference—0
Improved algorithm for neuronal ensemble inference by Monte Carlo method—0
A Bayesian/Information Theoretic Model of Bias Learning—0
Streaming Bayesian Inference for Crowdsourced Classification—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess HypothesesCode0
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set—0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Phase transitions and optimal algorithms for semi-supervised classifications on graphs: from belief propagation to graph convolution network—0
Continual Multi-task Gaussian ProcessesCode0
Parameter elimination in particle Gibbs samplingCode0
Bayesian causal inference via probabilistic program synthesis—0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset—0
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Benchmark Results

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