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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 626–650 of 2226 papers

TitleStatusHype
Calibration of Model Uncertainty for Dropout Variational Inference—0
Can Bayesian Neural Networks Make Confident Predictions?—0
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference—0
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control—0
Can Sequential Bayesian Inference Solve Continual Learning?—0
A Markov Model of Machine Translation using Non-parametric Bayesian Inference—0
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning—0
Cascaded Calibration of Mechatronic Systems via Bayesian Inference—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Causal Inference through a Witness Protection Program—0
Predictive Coding beyond Correlations—0
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties—0
Active inference and deep generative modeling for cognitive ultrasound—0
Augmented Message Passing Stein Variational Gradient Descent—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
Classified as unknown: A novel Bayesian neural network—0
Clustered Mallows Model—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
Coherent Track-Before-Detect—0
Cohort effects in mortality modelling: a Bayesian state-space approach—0
BARNN: A Bayesian Autoregressive and Recurrent Neural Network—0
Cold Posteriors through PAC-Bayes—0
Collapsed Variational Bayesian Inference for PCFGs—0
Big Learning with Bayesian Methods—0
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Benchmark Results

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