Decoding Latent Variables: Comparing Bayesian, EM, and VAE Approaches

Author(s): Shenggang Li

Originally published on Towards AI.

A Deep Dive into Mathematical Foundations, A/B Testing Applications, and Choosing the Right Method for Your Data Challenges.

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Ever wondered how to uncover hidden details in your data when things are not fully clear? Consider running an A/B test for a marketing campaign — sales numbers may be available, but the true impact could remain hidden. This paper explores three methods to address such challenges: Expectation-Maximization (EM), Bayesian estimation, and Variational Autoencoders (VAEs), each offering unique insights into latent variable analysis.

The EM algorithm addresses missing information by iteratively guessing and refining hidden details. In A/B testing, it is effective for both masked and fully observed treatments, bridging gaps in incomplete data. Bayesian estimation incorporates a probabilistic framework, combining prior knowledge with observed data to reveal not only results but also confidence levels, making it ideal for comparing group performance and probabilities.

VAEs, a prominent method in AI, are widely recognized for recreating images and generating data. However, they go beyond these applications by uncovering hidden patterns, creating a “latent space”, and simulating “what-if” scenarios. Unlike EM or Bayesian methods, VAEs generate new possibilities, making them particularly effective for exploratory analysis.

This paper examines how VAEs connect to traditional methods like EM and Bayesian estimation…. Read the full blog for free on Medium.

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Published via Towards AI