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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 341–350 of 2226 papers

TitleStatusHype
Bayesian Prediction-Powered Inference—0
Outlier-robust Kalman Filtering through Generalised BayesCode2
Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP—0
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference—0
Deep Learning and genetic algorithms for cosmological Bayesian inference speed-upCode0
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language ModelsCode1
Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context LearningCode1
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMsCode0
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Benchmark Results

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