Anatomy of the EXA Bayesian Engine: Mixture Distributions and Observational Deviation
This is the first article in a technical explanation series identifying the operating principles of the EXA engine, which played a major role in the novel-style series [BA03 On-Time Material Inbound: Bayesian MCMC]. Since this series covers Mixture Distributions and MCMC (Markov Chain Monte Carlo) Gibbs Sampling—which are advanced techniques in Bayesian inference—the content may be deep and the calculation process somewhat complex. Therefore, we intend to approach this in a detailed, step-by-step manner to make it as digestible as possible, and it is expected to be a fairly long journey. We recommend reading the original novel first to understand the overall context. Furthermore, as Bayesian theory expands its concepts incrementally, reviewing the episodes and mathematical explanations of BA01 and BA02 beforehand will be much more helpful in grasping this content. The preceding mathematical concepts and logic are being carried forward.
- Bayesian
- Gibbs Sampling
- likelihood