Auto-tuner Posts
Mastering Complexity
Explore EXA's Unified Intelligence ecosystem that distills complex business environments into clear conclusions and redefine your enterprise strategy.
BA024. The Evolution of EXAWin Bayesian Engine: The Day Data Tuned Its Own Parameters
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.
BA025. Finding the Optimal Boundary — The Math of Grid Search and Youden's J
How do you find the 'optimal' among 3,240 parameter combinations? Grid Search performs an exhaustive scan, and Youden's J Index finds the balance point between Sensitivity and Specificity. The mathematical principles behind data-driven tuning of sales stage weights (T) and signal sensitivity (k) — the first pillar of Auto-Tuner — explained with business context.
BA026. Consensus of the Particles — The Math of MCMC Ensembles 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.