Semantic Variational Bayes Method Proposed for Latent Variable Solving

A new Semantic Variational Bayes (SVB) method has been proposed to solve probability distributions of latent variables, addressing the complexity and unintuitive nature of the traditional Variational Bayesian (VB) method. The SVB method is derived from the rate-fidelity function R(G) within the Semantic Information Theory, which extends Shannon's rate-distortion function R(D). The author's previous work on Semantic Information Theory redefines R as the minimum mutual information for a given semantic mutual information G. The SVB method employs constraint functions including likelihood, truth, membership, similarity, and distortion, and uses the maximum information efficiency (G/R) criterion, which includes the maximum semantic information criterion for model parameter optimization. The variational and iterative techniques in SVB originate from Shannon et al.'s research on the rate-distortion function. This approach aims to provide a more interpretable and computationally efficient alternative to VB for latent variable modeling.