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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 976–1000 of 2226 papers

TitleStatusHype
Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach—0
Variational Inference for Bayesian Bridge Regression—0
DPER: Dynamic Programming for Exist-Random Stochastic SATCode0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling—0
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural networkCode0
DPO: Dynamic-Programming Optimization on Hybrid ConstraintsCode0
Intuitive and Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference—0
On the Convergence of the Shapley Value in Parametric Bayesian Learning GamesCode0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control—0
A Deep Learning Approach to Dst Index Prediction—0
Bézier Curve Gaussian Processes—0
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction—0
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference—0
Approximating Permutations with Neural Network Components for Travelling Photographer Problem—0
Deep Ensemble as a Gaussian Process Approximate Posterior—0
Statistical applications of contrastive learning—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
A Bayesian Approach To Graph Partitioning—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
A stochastic Stein Variational Newton methodCode0
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning—0
Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications—0
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Benchmark Results

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