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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 751–775 of 2226 papers

TitleStatusHype
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
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
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
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning—0
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies—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
Convolutional Bayesian Kernel Inference for 3D Semantic MappingCode1
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference—0
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian InferenceCode1
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
Batch Bayesian Optimization via Particle Gradient FlowsCode0
Implicit Full Waveform Inversion with Deep Neural Representation—0
Non-Gaussian Process Regression—0
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution—0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—0
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Benchmark Results

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