/workflow/index.html Purpose Give DSAMbayes users a workflow-shaped mental model for Bayesian MMM. This section is the methodological spine of the docs: it explains the sequence of decisions, assumptions, and diagnostic gates that should sit behind any DSAMbayes run. Audience Econometricians moving from OLS or other frequentist MMM workflows into Bayesian modelling. Analysts who know how to run DSAMbayes but want a more defensible modelling process. Reviewers who need to understand what a “good” DSAMbayes run should have passed before interpretation. Why this section exists DSAMbayes already documents its runner, model classes, priors, and diagnostics in detail. What most users still need is a clear answer to: Hugo en-gb /workflow/what-principled-means/index.html Mon, 01 Jan 0001 00:00:00 +0000 /workflow/what-principled-means/index.html Objective Define what DSAMbayes means by a principled Bayesian MMM workflow. The term is intentionally about process, not brand loyalty to a particular model class or sampler. A principled workflow is one where assumptions are explicit, diagnostics are stage-gated, and downstream interpretation is conditioned on those gates. The short definition A DSAMbayes workflow is principled when it does all of the following: /workflow/frequentist-to-bayesian-translation/index.html Mon, 01 Jan 0001 00:00:00 +0000 /workflow/frequentist-to-bayesian-translation/index.html Objective Translate familiar classical regression instincts into the DSAMbayes workflow so users coming from OLS, GLM, or general frequentist econometrics can adopt Bayesian MMM without losing methodological discipline. What does not change Moving to DSAMbayes does not remove the need for: careful data definition sensible controls thinking about omitted variables residual scrutiny skepticism about causal claims Bayesian MMM is not a shortcut around model design. It is a different way of expressing assumptions and uncertainty. /workflow/stage-2-model-and-priors/index.html Mon, 01 Jan 0001 00:00:00 +0000 /workflow/stage-2-model-and-priors/index.html Objective Specify a model that is explicit enough to be audited and simple enough to be defended. For most DSAMbayes users, this stage is where the biggest conceptual shift happens. In classical MMM, the common instinct is to choose variables, run the regression, and worry about coefficient stability afterwards. In DSAMbayes, priors and boundaries are part of the specification from the start. /workflow/stage-4-computation-and-sampler/index.html Mon, 01 Jan 0001 00:00:00 +0000 /workflow/stage-4-computation-and-sampler/index.html Objective Decide whether the posterior draws are numerically trustworthy. This stage is about computation quality, not business interpretation and not causal validity. If it fails, every downstream quantity that depends on posterior draws becomes unreliable. The key question Before asking whether the model is good, ask whether the sampler actually explored the posterior well enough for the summaries to mean what they appear to mean. /workflow/stage-5-model-adequacy/index.html Mon, 01 Jan 0001 00:00:00 +0000 /workflow/stage-5-model-adequacy/index.html Objective Decide whether the fitted model is a credible description of the observed data. This is the stage that sits between computational trust and business interpretation. A model can pass sampler diagnostics and still fail here. The key question If I simulate from the fitted model, does it reproduce the important structure of the observed data well enough for decomposition, comparison, and optimisation to be taken seriously?