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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 1051–1100 of 2226 papers

TitleStatusHype
A Bayesian/Information Theoretic Model of Bias Learning—0
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—0
Bayesian Inference Accelerator for Spiking Neural Networks—0
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm—0
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
Differentially Private Bayesian Inference for Generalized Linear Models—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs—0
Device Detection and Channel Estimation in MTC with Correlated Activity Pattern—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Bayesian Hypernetworks—0
An Interpretable Neural Network for Parameter Inference—0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
De-randomizing MCMC dynamics with the diffusion Stein operator—0
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation—0
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition—0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
Density Estimation via Bayesian Inference Engines—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Bayesian geoacoustic inversion using mixture density network—0
Deep Stable neural networks: large-width asymptotics and convergence rates—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Bayesian Flow Networks in Continual Learning—0
A Neural Implementation of the Kalman Filter—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models—0
A Bayesian Inference Framework for Procedural Material Parameter Estimation—0
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning—0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods—0
Deep Maxout Network Gaussian Process—0
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Benchmark Results

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