meanfieldinferens
Meanfieldinferens refers to mean-field inference, a common approach for approximating complex posterior distributions in probabilistic models. It is widely used in variational Bayesian methods and is sometimes encountered under variant spellings such as meanfieldinferens. The core idea is to replace an intractable posterior p(z|x) with a simpler distribution q(z) that factorizes over groups of latent variables, reducing a high-dimensional problem to a set of manageable updates.
The mean-field approximation assumes that the latent variables are approximately independent under q, so q(z) = ∏_i
Advantages of mean-field inference include scalability to large datasets and models, and the ability to provide
Applications span topic models (such as latent Dirichlet allocation), mixture and hidden Markov models, Bayesian neural