SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 876–900 of 2226 papers

TitleStatusHype
Bottom-up data integration in polymer models of chromatin organisation—0
On Representations of Mean-Field Variational Inference—0
Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery—0
Variational Model Perturbation for Source-Free Domain AdaptationCode0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Data Subsampling for Bayesian Neural NetworksCode0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
On Divergence Measures for Bayesian PseudocoresetsCode0
Sampling-based inference for large linear models, with application to linearised LaplaceCode0
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion ModelsCode0
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
Robust Bayesian Inference for Moving Horizon Estimation—0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Amortized Bayesian Inference of GISAXS Data with Normalizing FlowsCode0
Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks—0
Generalized second law of thermodynamics in the Glosten-Milgrom model—0
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies—0
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning—0
Hamiltonian Adaptive Importance Sampling—0
Ensemble-based gradient inference for particle methods in optimization and samplingCode0
Batch Bayesian optimisation via density-ratio estimation with guaranteesCode0
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference—0
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Benchmark Results

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