Variational E-M versus Gibbs sampling

These two approaches offer different trade-offs in terms of computational efficiency and accuracy. I think it is worthwhile describing these here. Variational E-M is preferred for efficiency and scalability when dealing with large datasets and when a close approximation to the posterior distribution is acceptable. Gibbs sampling is preferred when higher accuracy is needed, and computational resources allow for the additional computational cost. Variational E-M is computationally efficient and often faster than Gibbs sampling. It provides a closed-form update for the variational parameters and can be scaled to large datasets and high-dimensional topic models. However, the quality of the approximation depends on the chosen variational family and can be less accurate than Gibbs sampling in capturing the true posterior distribution. Gibbs sampling provides a more accurate estimation of the posterior distribution compared to variational inference. It...