Mcmc Posts
Mastering Complexity
Explore EXA's Unified Intelligence ecosystem that distills complex business environments into clear conclusions and redefine your enterprise strategy.
The Real Game in Business Is the Fight Against Uncertainty
BA03. [On-Time Material Inbound: Bayesian MCMC] The Real Game in Business is the Fight Against Uncertainty
The Silent Factory: Bayesian Diagnosis of a Short Shot
Quantifying the realm of intuition: A case study of dynamic decision-making using Bayesian updates. How does data become a weapon for decision-making in a manufacturing site ruled by uncertainty? This article vividly shows a real-world application of Bayesian statistics through the process of resolving 'Short Shot' defects in an injection molding factory.
When Sales Data Tunes the Bayesian Engine
The EXA Bayesian Engine calculated win probabilities, but its precision depended on manually configured initial parameters. When 100 historical deals accumulated, the engine was ready to evolve on its own. Grid Search, MCMC Ensemble Sampling, and Cross-Validation — three mathematical pillars working in concert to find optimal parameters. Told as a story.
Testing Parameter Consensus with MCMC and Cross-Validation
If Grid Search found the 'tallest hill,' the MCMC Ensemble Sampler is the process by which 256 explorers reach consensus that 'the height is correct.' The mathematical principles behind Emcee's affine-invariant walkers, R̂ convergence diagnostics, HDI 95% credible intervals, 5-Fold cross-validation, and Signal Lift analysis — explained with business context.
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.