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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 301–350 of 2226 papers

TitleStatusHype
Off-grid Channel Estimation for Orthogonal Delay-Doppler Division Multiplexing Using Grid Refinement and Adjustment—0
Predictive Coding Networks and Inference Learning: Tutorial and Survey—0
Electrostatics-based particle sampling and approximate inferenceCode0
Joint Channel and Data Estimation for Multiuser Extremely Large-Scale MIMO Systems—0
Torchtree: flexible phylogenetic model development and inference using PyTorchCode1
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient FlowsCode2
Hierarchical thematic classification of major conference proceedings—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Bayesian Inference for Multidimensional Welfare Comparisons—0
Conditional score-based diffusion models for solving inverse problems in mechanics—0
Integrating time-resolved nrf2 gene-expression data into a full GUTS model as a proxy for toxicodynamic damage in zebrafish embryo—0
DistPred: A Distribution-Free Probabilistic Inference Method for Regression and ForecastingCode2
Electricity Spot Prices Forecasting Using Stochastic Volatility ModelsCode0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
Verbalized Probabilistic Graphical Modeling with Large Language Models—0
Stochastic full waveform inversion with deep generative prior for uncertainty quantification—0
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians—0
Reparameterization invariance in approximate Bayesian inference—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors—0
Is In-Context Learning in Large Language Models Bayesian? A Martingale PerspectiveCode0
Logistic Variational Bayes RevisitedCode0
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noiseCode0
Bayesian Online Natural Gradient (BONG)Code0
Understanding and mitigating difficulties in posterior predictive evaluation—0
Kernel Semi-Implicit Variational InferenceCode0
MAGIC: Modular Auto-encoder for Generalisable Model Inversion with Bias CorrectionsCode0
Infinite-dimensional Diffusion Bridge Simulation via Operator LearningCode0
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks—0
Bayesian Inference with Deep Weakly Nonlinear Networks—0
A hierarchical Bayesian model for syntactic priming—0
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection ExperimentsCode0
Poisson Variational AutoencoderCode1
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation—0
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI CapabilitiesCode0
Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models—0
Estimating Idea Production: A Methodological SurveyCode0
Bayesian Prediction-Powered Inference—0
Outlier-robust Kalman Filtering through Generalised BayesCode2
Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP—0
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference—0
Deep Learning and genetic algorithms for cosmological Bayesian inference speed-upCode0
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language ModelsCode1
Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context LearningCode1
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMsCode0
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Benchmark Results

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