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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 1651–1700 of 2226 papers

TitleStatusHype
Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model—0
Loss-calibrated expectation propagation for approximate Bayesian decision-making—0
Low-pass filtering as Bayesian inference—0
Low-rank matrix reconstruction and clustering via approximate message passing—0
Lp : A Logic for Statistical Information—0
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations—0
Machine Learning and the Future of Bayesian Computation—0
Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)—0
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set—0
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications—0
Manifold Optimization Assisted Gaussian Variational Approximation—0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling—0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
Marginalizing Corrupted Features—0
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Marginal sequential Monte Carlo for doubly intractable models—0
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap—0
Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior—0
Martingale Posterior Neural Processes—0
Massively Parallel Reweighted Wake-Sleep—0
Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against Myeloproliferative Neoplasms—0
Max-Margin Majority Voting for Learning from Crowds—0
MC^2RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference—0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
MCMC-driven importance samplers—0
Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization—0
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues—0
Mean Field Network based Graph Refinement with application to Airway Tree Extraction—0
Mean-field theory of graph neural networks in graph partitioning—0
Mean field variational Bayesian inference for support vector machine classification—0
Mean-field Variational Inference via Wasserstein Gradient Flow—0
Merging MCMC Subposteriors through Gaussian-Process Approximations—0
Message Passing Stein Variational Gradient Descent—0
Meta-Learning for Variational Inference—0
Meta-Learning Divergences of Variational Inference—0
Metropolis-CVAE: Bootstrapping Labels for Bayesian Inference via Semi-Supervised Conditional Variational Autoencoders—0
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study—0
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference—0
Metropolis-Hastings view on variational inference and adversarial training—0
Metropolis Sampling—0
MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Minimum Description Length Control—0
Mixability in Statistical Learning—0
Mixture Distributions for Scalable Bayesian Inference—0
Mixture modeling on related samples by ψ-stick breaking and kernel perturbation—0
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis—0
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit—0
Model-based Bayesian Reinforcement Learning for Dialogue Management—0
Model-based Influence Diagrams for Machine Vision—0
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Benchmark Results

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