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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 701–750 of 2226 papers

TitleStatusHype
Bistable perception, precision and neuromodulationCode0
Bayesian posterior approximation with stochastic ensemblesCode0
Bayesian data fusion with shared priors—0
Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework—0
A compartmental model for Xylella fastidiosa diseases with explicit vector seasonal dynamics—0
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models—0
PRISM: Probabilistic Real-Time Inference in Spatial World Models—0
Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems—0
Are you using test log-likelihood correctly?—0
Trust and Time Preference: Measuring a Causal Effect in a Random-Assignment Experiment—0
Double Robust Bayesian Inference on Average Treatment Effects—0
A Search and Detection Autonomous Drone System: from Design to Implementation—0
Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive ModelsCode1
Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization—0
Bayesian Learning for Neural Networks: an algorithmic survey—0
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World SurveillanceCode1
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventionsCode0
Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densities—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Bayesian score calibration for approximate modelsCode0
Generalization of generative model for neuronal ensemble inference method—0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Black-box Coreset Variational InferenceCode0
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Ensemble transport smoothing. Part II: Nonlinear updatesCode0
Ensemble transport smoothing. Part I: Unified frameworkCode0
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms—0
SoftBart: Soft Bayesian Additive Regression Trees—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC—0
Bayesian Methods in Automated Vehicle's Car-following Uncertainties: Enabling Strategic Decision Making—0
Learning Latent Structural Causal Models—0
GFlowOut: Dropout with Generative Flow Networks—0
Bayesian inference is facilitated by modular neural networks with different time scalesCode0
Uncertain Evidence in Probabilistic Models and Stochastic Simulators—0
Efficient identification of informative features in simulation-based inferenceCode0
Bayesian deep learning framework for uncertainty quantification in high dimensions—0
Bottom-up data integration in polymer models of chromatin organisation—0
Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery—0
On Representations of Mean-Field Variational Inference—0
Variational Model Perturbation for Source-Free Domain AdaptationCode0
Data Subsampling for Bayesian Neural NetworksCode0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
On Divergence Measures for Bayesian PseudocoresetsCode0
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion ModelsCode0
Sampling-based inference for large linear models, with application to linearised LaplaceCode0
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Benchmark Results

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