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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 1551–1600 of 2226 papers

TitleStatusHype
Adaptive Gaussian Copula ABC—0
Adaptive Gaussian process surrogates for Bayesian inference—0
Adaptive matching pursuit for sparse signal recovery—0
Adaptive mitigation of time-varying quantum noise—0
Adaptive modelling of anti-tau treatments for neurodegenerative disorders based on the Bayesian approach with physics-informed neural networks—0
Adaptive particle-based approximations of the Gibbs posterior for inverse problems—0
Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition—0
A Deep Learning Approach to Dst Index Prediction—0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models—0
A Distributed Framework for the Construction of Transport Maps—0
Adjoint-aided inference of Gaussian process driven differential equations—0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference—0
Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach—0
Adversarial Message Passing For Graphical Models—0
A Factor Graph Model of Trust for a Collaborative Multi-Agent System—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks—0
A Formal Calculus for International Relations Computation and Evaluation—0
A Generalization Bound for Online Variational Inference—0
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes—0
A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs—0
A Group-Wise Narrow Beam Design for Uplink Channel Estimation in Hybrid Beamforming Systems—0
A hierarchical Bayesian model for syntactic priming—0
A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation—0
AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers—0
AI-Powered Bayesian Inference—0
A Kernel Learning Method for Backward SDE Filter—0
A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies—0
A Latent Space Theory for Emergent Abilities in Large Language Models—0
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees—0
Algorithms for Inference in SVARs Identified with Sign and Zero Restrictions—0
Algorithms of the LDA model [REPORT]—0
Aligned Multi-Task Gaussian Process—0
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
A Markov Model of Machine Translation using Non-parametric Bayesian Inference—0
A Mathematical Trust Algebra for International Nation Relations Computation and Evaluation—0
A Mathematical Walkthrough and Discussion of the Free Energy Principle—0
A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression—0
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
Variational inference of fractional Brownian motion with linear computational complexity—0
Amortized Bayesian Inference for Models of Cognition—0
Amortized Bayesian Mixture Models—0
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Benchmark Results

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