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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 651–700 of 2226 papers

TitleStatusHype
Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET—0
PyVBMC: Efficient Bayesian inference in PythonCode1
Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference—0
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian InferenceCode0
Simulation-based Bayesian inference for robotic grasping—0
Fast post-process Bayesian inference with Variational Sparse Bayesian QuadratureCode0
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal MetricsCode0
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamicsCode0
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering—0
A normative theory of social conflict—0
Bayesian inference with finitely wide neural networks—0
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics—0
Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme—0
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference—0
Deep Learning Enhanced Realized GARCHCode0
Parameters for > 300 million Gaia stars: Bayesian inference vs. machine learning—0
Event Temporal Relation Extraction with Bayesian Translational Model—0
Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis—0
Trading Information between Latents in Hierarchical Variational AutoencodersCode0
Geometry of Score Based Generative Models—0
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetsCode1
Fortuna: A Library for Uncertainty Quantification in Deep LearningCode2
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Quantifying tissue growth, shape and collision via continuum models and Bayesian inferenceCode0
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field—0
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexityCode0
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity—0
QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative ModelsCode1
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularizationCode0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
Classified as unknown: A novel Bayesian neural network—0
Differentially Private Distributed Bayesian Linear Regression with MCMCCode0
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free InferenceCode0
Coin Sampling: Gradient-Based Bayesian Inference without Learning RatesCode0
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time SeriesCode1
Projective Integral Updates for High-Dimensional Variational InferenceCode0
Robust Gaussian Process Regression with Huber Likelihood—0
Hierarchical Bayesian inference for community detection and connectivity of functional brain networksCode0
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems—0
Reinforcement Learning Enhanced PicHunter for Interactive Search—0
On Sequential Bayesian Inference for Continual LearningCode0
Geometric Ergodicity in Modified Variations of Riemannian Manifold and Lagrangian Monte Carlo—0
Relative Probability on Finite Outcome Spaces: A Systematic Examination of its Axiomatization, Properties, and Applications—0
Bayesian Interpolation with Deep Linear Networks—0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Physics-Informed Gaussian Process Regression Generalizes Linear PDE SolversCode1
The Inverse of Exact Renormalization Group Flows as Statistical Inference—0
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Benchmark Results

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