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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 326–350 of 2226 papers

TitleStatusHype
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