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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 701–725 of 2226 papers

TitleStatusHype
Analogy as Nonparametric Bayesian Inference over Relational Systems—0
Bidirectional Recurrent Neural Networks as Generative Models—0
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models—0
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties—0
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity—0
Copula Processes—0
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Correcting Mode Proportion Bias in Generalized Bayesian Inference via a Weighted Kernel Stein Discrepancy—0
Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach—0
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging—0
Cortical Microcircuits from a Generative Vision Model—0
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation—0
Dynamics on Lie groups with applications to attitude estimation—0
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators—0
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference—0
BIBI: Bayesian Inference of Breed Composition—0
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making—0
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
A compartmental model for Xylella fastidiosa diseases with explicit vector seasonal dynamics—0
DART: Depth-Enhanced Accurate and Real-Time Background Matting—0
Data augmentation in Bayesian neural networks and the cold posterior effect—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
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Benchmark Results

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