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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 1876–1900 of 2226 papers

TitleStatusHype
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization—0
Roll-back Hamiltonian Monte Carlo—0
Inverse Ising problem in continuous time: A latent variable approachCode0
Learning Inference Models for Computer Vision—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
PROBE-GK: Predictive Robust Estimation using Generalized Kernels—0
Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds—0
Learning to Draw Samples with Amortized Stein Variational Gradient Descent—0
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior—0
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix FactorisationCode0
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference—0
Stochastic Gradient MCMC Methods for Hidden Markov Models—0
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks—0
Streaming Bayesian inference: theoretical limits and mini-batch approximate message-passing—0
Efficient Low-Order Approximation of First-Passage Time Distributions—0
Variational Sequential Monte CarloCode0
Bayesian stochastic blockmodeling—0
Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment—0
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models—0
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models—0
Approximate Bayesian inference as a gauge theory—0
Joint Positioning and Radio Map Generation Based on Stochastic Variational Bayesian Inference for FWIPS—0
Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach—0
Constrained Bayesian Networks: Theory, Optimization, and Applications—0
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Benchmark Results

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