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Bayesian Statistics
A statistical approach that updates beliefs with data.
Definition
Bayesian statistics combine prior information with observed data to estimate parameters.
How to Calculate
Specify a prior, define a likelihood, and compute a posterior distribution.
Why it Matters
Useful for small samples and hierarchical models in CPG.
Key Advantages
Handles uncertainty and partial pooling well.
Example
Estimate lift for many small SKUs with a shared prior.
Use it to
Apply probabilistic models that update beliefs as new data arrives to estimate parameters with uncertainty. Use posterior distributions to compare scenarios and make decisions that account for variance rather than single-point estimates.
