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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 601–650 of 2226 papers

TitleStatusHype
AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data—0
A Mathematical Walkthrough and Discussion of the Free Energy Principle—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models—0
Blindness of score-based methods to isolated components and mixing proportions—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
A Mathematical Trust Algebra for International Nation Relations Computation and Evaluation—0
Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability—0
Bootstrapped synthetic likelihood—0
Bounded rationality in structured density estimation—0
Bounded rationality in structured density estimation—0
Bridging the reality gap in quantum devices with physics-aware machine learning—0
Bridging the Sim-to-Real Gap with Bayesian Inference—0
Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation—0
Biologically Inspired Dynamic Textures for Probing Motion Perception—0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
A Unified Kernel for Neural Network Learning—0
Building general Langevin models from discrete data sets—0
Burn-in, bias, and the rationality of anchoring—0
Automatic Variational Inference in Stan—0
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution—0
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting—0
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications—0
Calibration of Model Uncertainty for Dropout Variational Inference—0
Can Bayesian Neural Networks Make Confident Predictions?—0
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference—0
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control—0
Can Sequential Bayesian Inference Solve Continual Learning?—0
A Markov Model of Machine Translation using Non-parametric Bayesian Inference—0
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning—0
Cascaded Calibration of Mechatronic Systems via Bayesian Inference—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Causal Inference through a Witness Protection Program—0
Predictive Coding beyond Correlations—0
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties—0
Active inference and deep generative modeling for cognitive ultrasound—0
Augmented Message Passing Stein Variational Gradient Descent—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
Classified as unknown: A novel Bayesian neural network—0
Clustered Mallows Model—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
Coherent Track-Before-Detect—0
Cohort effects in mortality modelling: a Bayesian state-space approach—0
BARNN: A Bayesian Autoregressive and Recurrent Neural Network—0
Cold Posteriors through PAC-Bayes—0
Collapsed Variational Bayesian Inference for PCFGs—0
Big Learning with Bayesian Methods—0
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Benchmark Results

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