Variational Bayes Made Easy
arXiv:2304.14251v23 citationsh-index: 28
Originality Synthesis-oriented
AI Analysis
This is an incremental improvement for researchers and practitioners using Variational Bayes.
The paper tackles the complexity of deriving Variational Bayes by introducing a 3-step recipe that simplifies the process, making it easier, faster, and more general.
Variational Bayes is a popular method for approximate inference but its derivation can be cumbersome. To simplify the process, we give a 3-step recipe to identify the posterior form by explicitly looking for linearity with respect to expectations of well-known distributions. We can then directly write the update by simply ``reading-off'' the terms in front of those expectations. The recipe makes the derivation easier, faster, shorter, and more general.