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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 651–700 of 2226 papers

TitleStatusHype
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Active pooling design in group testing based on Bayesian posterior prediction—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Communication-Efficient Distributed Statistical Inference—0
BAT.jl -- A Julia-based tool for Bayesian inference—0
Comparative Study of Inference Methods for Interpolative Decomposition—0
A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction—0
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
Compositional amortized inference for large-scale hierarchical Bayesian models—0
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models—0
Active recursive Bayesian inference using Rényi information measures—0
A Mathematical Trust Algebra for International Nation Relations Computation and Evaluation—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Biologically Inspired Dynamic Textures for Probing Motion Perception—0
Computing the quality of the Laplace approximation—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
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior—0
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting—0
Adams Conditioning and Likelihood Ratio Transfer Mediated Inference—0
A Markov Model of Machine Translation using Non-parametric Bayesian Inference—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Confidence Estimation via Sequential Likelihood Mixing—0
Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges—0
Confident in the Crowd: Bayesian Inference to Improve Data Labelling in Crowdsourcing—0
Conjugate Natural Selection—0
Connections between sequential Bayesian inference and evolutionary dynamics—0
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems—0
Augmented Message Passing Stein Variational Gradient Descent—0
Big Learning with Bayesian Methods—0
Constrained Bayesian Inference for Low Rank Multitask Learning—0
Constrained Bayesian Networks: Theory, Optimization, and Applications—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference—0
Consumer Demand Modeling During COVID-19 Pandemic—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Bidirectional Recurrent Neural Networks as Generative Models—0
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models—0
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Benchmark Results

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