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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 261–270 of 2226 papers

TitleStatusHype
SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation GenerationCode0
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods—0
Variational Bayesian Inference for Time-Varying Massive MIMO Channels: Estimation and Detection—0
Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference NetworkCode0
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making—0
Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching—0
Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance SpectroscopyCode0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
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Benchmark Results

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