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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 1601–1650 of 2226 papers

TitleStatusHype
Kalman filters as the steady-state solution of gradient descent on variational free energy—0
Kernel Bayesian Inference with Posterior Regularization—0
Kernel Bayes' Rule—0
Kernel Stein Generative Modeling—0
Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks—0
Online Label Aggregation: A Variational Bayesian Approach—0
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process—0
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise—0
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments—0
Latent Variable Models for Bayesian Causal Discovery—0
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Learning and Inference in Hilbert Space with Quantum Graphical Models—0
Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications—0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
Learning Curves for Deep Neural Networks: A field theory perspective—0
Learning Deep Generative Models with Doubly Stochastic MCMC—0
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Learning Inference Models for Computer Vision—0
Learning Latent Structural Causal Models—0
Learning Manifold Implicitly via Explicit Heat-Kernel Learning—0
Learning non-concatenative morphology—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Learning optimal Bayesian prior probabilities from data—0
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs—0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Learning to Draw Samples with Amortized Stein Variational Gradient Descent—0
Learning with Compressible Priors—0
Learning without Recall by Random Walks on Directed Graphs—0
Learn to Estimate Labels Uncertainty for Quality Assurance—0
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design—0
Lévy walks derived from a Bayesian decision-making model in non-stationary environments—0
Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching—0
Likelihood-free inference via classification—0
Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis—0
Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network—0
Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics—0
Linking fast and slow: the case for generative models—0
LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation—0
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization—0
Localized Schrödinger Bridge Sampler—0
Localizing Bugs in Program Executions with Graphical Models—0
Locally Adaptive Bayesian Multivariate Time Series—0
Locally adaptive factor processes for multivariate time series—0
Locally Differentially Private Bayesian Inference—0
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches—0
Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
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Benchmark Results

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