This quiz covers priors, posterior sampling, credible intervals, and diagnostic metrics in Bayesian marketing‑mix models.
Bayesian MMM treats model parameters as ______ variables with prior distributions.
fixed
deterministic
random
binary
The 2025 Robyn‑Next library switches its prior for TV adstock half‑life to a ______ distribution.
Normal
Uniform
Bernoulli
Gamma
Posterior samples are generated via ______ algorithms such as NUTS in Stan.
MCMC
PCA
OLS
Gradient descent
Bayesian MMM naturally produces ______ intervals around ROI estimates.
confidence
credible
prediction
control
Hierarchical priors 'shrink' noisy channel coefficients toward the _____ mean.
maximum
zero
observed
group
Convergence diagnostics like R‑hat should be below ______ for reliable inference.
1.1
5
2
0.5
The 2025 Google LightweightMMM adds automatic prior scaling based on ______ variance.
cookie
input spend
creative
residual
Posterior predictive checks compare simulated outcomes to ______ data.
cookie
observed
benchmark
proxy
Evidence Lower Bound (ELBO) is used when training MMM via variational ______.
propagation
aggregation
inference
sampling
Informative priors from prior studies help when media spend exhibits ______ issues.
zero decay
limited variation
full collinearity
perfect independence
Starter
Review the basics.
Solid
Nice work—refine the details.
Expert!
Exceptional command of the topic.
Mastering Bayesian MMM Interview Questions demonstrates your skill with probabilistic models for assigning credit across channels. Start by reviewing the marketing mix modelling interview questions to see how Bayesian inference fits into your attribution toolkit. Next, test your grasp of key metrics with the KPI alignment interview MCQs and walk through the four-step MMM process interview guide for a clear, step-by-step framework. Finally, sharpen your analysis by tackling the multicollinearity remedies interview questions to keep your models both accurate and reliable.
