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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 1251–1300 of 2226 papers

TitleStatusHype
Weighted Mean Curvature—0
What does the free energy principle tell us about the brain?—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Why bigger is not always better: on finite and infinite neural networks—0
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits—0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors—0
Decentralized Bayesian Learning over Graphs—0
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo—0
Bottom-up data integration in polymer models of chromatin organisation—0
Bridging Privacy and Robustness for Trustworthy Machine Learning—0
Decipherment Complexity in 1:1 Substitution Ciphers—0
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control—0
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Decoupled Variational Gaussian Inference—0
Deep Active Inference for Autonomous Robot Navigation—0
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference—0
Deep de Finetti: Recovering Topic Distributions from Large Language Models—0
Deep Ensemble as a Gaussian Process Approximate Posterior—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Deep Knowledge Tracing with Learning Curves—0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Deep Learning and Bayesian inference for Inverse Problems—0
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI—0
Deep Learning Surrogates for Real-Time Gas Emission Inversion—0
Deep Maxout Network Gaussian Process—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Stable neural networks: large-width asymptotics and convergence rates—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Density Estimation via Bayesian Inference Engines—0
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation—0
De-randomizing MCMC dynamics with the diffusion Stein operator—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Device Detection and Channel Estimation in MTC with Correlated Activity Pattern—0
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs—0
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers—0
Differentially Private Bayesian Inference for Generalized Linear Models—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—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
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Benchmark Results

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