SOTAVerified

Bayesian Inference

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1475 of 2226 papers

TitleStatusHype
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling—0
Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach—0
A Technical Critique of Some Parts of the Free Energy Principle—0
Probabilistic Reasoning across the Causal Hierarchy—0
Bayesian task embedding for few-shot Bayesian optimizationCode0
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference—0
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Development of Use-specific High Performance Cyber-Nanomaterial Optical Detectors by Effective Choice of Machine Learning AlgorithmsCode0
Attention-Aware Answers of the Crowd—0
Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continuaCode0
Quantile Propagation for Wasserstein-Approximate Gaussian ProcessesCode0
NFAD: Fixing anomaly detection using normalizing flowsCode0
Normalizing Constant Estimation with Gaussianized Bridge SamplingCode0
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers—0
On the relationship between multitask neural networks and multitask Gaussian Processes—0
A Closer Look at Disentangling in β-VAE—0
Hidden Markov Model: Tutorial—0
Neural Tangents: Fast and Easy Infinite Neural Networks in PythonCode0
Overcoming Catastrophic Forgetting by Generative Regularization—0
A Bayesian Inference Framework for Procedural Material Parameter Estimation—0
On the geometry of Stein variational gradient descent—0
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon SynapseCode0
Show:102550
← PrevPage 59 of 90Next →

Benchmark Results

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