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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 901–925 of 2226 papers

TitleStatusHype
Efficient acquisition rules for model-based approximate Bayesian computation—0
Bayesian inference for spatio-temporal spike-and-slab priors—0
Dynamics on Lie groups with applications to attitude estimation—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Bayesian Inference for Radar Imagery Based Surveillance—0
Epidemic mitigation by statistical inference from contact tracing data—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
Episodic memory for continual model learning—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data—0
Estimating Compact Yet Rich Tree Insertion Grammars—0
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
Data Augementation with Polya Inverse Gamma—0
A Parameter-Free Learning Automaton Scheme—0
Estimating the impact of non-pharmaceutical interventions and vaccination on the progress of the COVID-19 epidemic in Mexico: a mathematical approach—0
Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data—0
A Distributed Framework for the Construction of Transport Maps—0
Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control—0
Bayesian Inference for Optimal Transport with Stochastic Cost—0
Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
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Benchmark Results

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