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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 1951–2000 of 2226 papers

TitleStatusHype
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Communication-Efficient Distributed Statistical Inference—0
Comparative Study of Inference Methods for Interpolative Decomposition—0
Comparing latent inequality with ordinal data—0
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches—0
Compositional amortized inference for large-scale hierarchical Bayesian models—0
Compressed Monte Carlo with application in particle filtering—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Compressed sensing reconstruction using Expectation Propagation—0
Computing the quality of the Laplace approximation—0
Concentration of the matrix-valued minimum mean-square error in optimal Bayesian inference—0
Conditional score-based diffusion models for solving inverse problems in mechanics—0
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Confidence Estimation via Sequential Likelihood Mixing—0
Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges—0
Confident in the Crowd: Bayesian Inference to Improve Data Labelling in Crowdsourcing—0
Conjugate Natural Selection—0
Connections between sequential Bayesian inference and evolutionary dynamics—0
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems—0
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Constrained Bayesian Inference for Low Rank Multitask Learning—0
Constrained Bayesian Networks: Theory, Optimization, and Applications—0
Constrained belief updates explain geometric structures in transformer representations—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks—0
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference—0
Consumer Demand Modeling During COVID-19 Pandemic—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting—0
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties—0
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity—0
Copula Processes—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Correcting Mode Proportion Bias in Generalized Bayesian Inference via a Weighted Kernel Stein Discrepancy—0
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging—0
Cortical Microcircuits from a Generative Vision Model—0
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators—0
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference—0
DART: Depth-Enhanced Accurate and Real-Time Background Matting—0
Data augmentation in Bayesian neural networks and the cold posterior effect—0
Data fission: splitting a single data point—0
Physics-constrained Bayesian inference of state functions in classical density-functional theory—0
Data-Driven Verification under Signal Temporal Logic Constraints—0
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems—0
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction—0
Decentralized Bayesian Learning over Graphs—0
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Benchmark Results

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