arXiv:2103.01327
공식 코드 · Papers with Code
A practical tutorial on Variational Bayes
원문 초록
This tutorial gives a quick introduction to Variational Bayes (VB), also called Variational Inference or Variational Approximation, from a practical point of view. The paper covers a range of commonly used VB methods and an attempt is made to keep the materials accessible to the wide community of data analysis practitioners. The aim is that the reader can quickly derive and implement their first VB algorithm for Bayesian inference with their data analysis problem. An end-user software package in Matlab together with the documentation can be found at https://vbayeslab.github.io/VBLabDocs/
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Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Variational Inference with Mixtures of Isotropic Gaussians
Amortized variational transdimensional inference
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
Can Transformers Learn Full Bayesian Inference in Context?
Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems
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