SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 276–300 of 2226 papers

TitleStatusHype
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC—0
Accelerating MCMC via Parallel Predictive Prefetching—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Approximate Bayesian inference as a gauge theory—0
Accelerating Langevin Sampling with Birth-death—0
Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs—0
Applications of the Free Energy Principle to Machine Learning and Neuroscience—0
Adjoint-aided inference of Gaussian process driven differential equations—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
Bayesian Deep Learning for Discrete Choice—0
A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series—0
A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation—0
A Distributed Framework for the Construction of Transport Maps—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
A Parameter-Free Learning Automaton Scheme—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem—0
An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring—0
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Bayesian data fusion with shared priors—0
Bayesian deep learning framework for uncertainty quantification in high dimensions—0
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Benchmark Results

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