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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 526–550 of 2226 papers

TitleStatusHype
Infinite-dimensional Diffusion Bridge Simulation via Operator LearningCode0
Bayesian Inference with Deep Weakly Nonlinear Networks—0
A hierarchical Bayesian model for syntactic priming—0
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection ExperimentsCode0
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation—0
Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI CapabilitiesCode0
Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models—0
Estimating Idea Production: A Methodological SurveyCode0
Bayesian Prediction-Powered Inference—0
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference—0
Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP—0
Deep Learning and genetic algorithms for cosmological Bayesian inference speed-upCode0
Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMsCode0
Network reconstruction via the minimum description length principle—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models—0
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design—0
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks—0
Likelihood Based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing ConstantsCode0
Accurate Direct Positioning in Distributed MIMO Using Delay-Doppler Channel Measurements—0
Uncertainty in latent representations of variational autoencoders optimized for visual tasksCode0
Variational Bayesian surrogate modelling with application to robust design optimisation—0
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Benchmark Results

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