Tag: bayesian
All the articles with the tag "bayesian".
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Coincidence Is Not Contribution
A marketing-mix model answers a causal question — what would sales have been without this media — not "what moved together." Most MMMs quietly answer the easier question and dress it up as the hard one.
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What Decades of Marketing-Mix Data Actually Tell Us
Forty years of scanner data and split-cable experiments converge on a few numbers that are remarkably stable across brands and categories. Knowing them turns a media model from a free-for-all into something with priors.
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Designing Experiments to Maximize Information
There's a single objective that says what makes one experiment better than another — expected information gain. It's beautiful, it's principled, and it's a nightmare to compute. Here's the arc from Lindley in 1956 to policies that design experiments in real time.
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The Illusion of Significance: Why p-value Variable Selection Breaks Marketing Mix Models
Stepwise regression produces overconfident models with biased estimates — here's why that matters for MMM, and how Bayesian shrinkage priors fix the problem without breaking your causal identification.