Glossary · Business Analytics
Bayesian Inference
also: Bayesian statistics · Bayes' theorem
Definition
Bayesian inference updates prior beliefs about a parameter using observed data via Bayes' theorem to produce a posterior distribution. In A/B testing it directly answers 'what is the probability that B beats A?', the question product teams actually ask, unlike the indirect counterfactual framing of frequentist p-values.
Bayes' theorem P(θ|D) = P(D|θ)·P(θ) / P(D) is the foundation. Practically, the posterior is often analytically tractable for conjugate models (Beta-Binomial for conversion testing) or sampled via MCMC for complex hierarchical models. Bayesian A/B testing allows continuous monitoring without inflating error rates, eliminates the peeking problem, and produces directly interpretable probabilities and expected-loss estimates.
Essays on this concept
- Business Analytics
Bayesian A/B Testing in Practice: When to Stop Experiments and How to Communicate Results to Non-Technical Stakeholders
Frequentist A/B testing answers a question nobody asked: 'If the null hypothesis were true, how surprising is this data?' Bayesian testing answers the question that matters: 'Given this data, what's the probability that B is actually better?'
- Marketing Engineering
Marketing Mix Modeling in the Privacy-First Era: Bayesian Structural Time Series Without User-Level Data
Cookies are dying. Deterministic attribution is shrinking. The irony: the measurement approach from the 1960s, Marketing Mix Modeling, is making a comeback, now powered by Bayesian inference that would have been computationally impossible when it was first invented.
- Marketing Engineering
Unified Measurement Architecture: Connecting MMM, MTA, and Experimentation Into a Single Source of Truth
MMM says Facebook works. MTA says Google works. The incrementality test says neither works as well as you thought. Three measurement systems, three different answers, here's how to reconcile them into one coherent picture.
Related concepts
Authoritative references