buyer_nu_posterior#
- pymc_marketing.customer_choice.taste_profiles.buyer_nu_posterior(model, n_samples=300)[source]#
Posterior of the average buyer’s taste vector per market.
For each posterior sample \(s\), market \(m\), and random-coef dimension \(d\),
\[\bar\nu_{m,d}^{(s)} = \frac{\sum_r \nu_{r,d} \cdot s^{\mathrm{in}}_{m,r}(s)} {\sum_r s^{\mathrm{in}}_{m,r}(s)}\]where \(s^{\mathrm{in}}_{m,r}\) sums the per-consumer-type inside-good probability across products.
- Parameters:
- model
BayesianBLP A fitted model.
- n_samples
int Number of posterior draws to use.
- model
- Returns:
np.ndarrayShape
(n_samples, M, D). The posterior of the average buyer’s taste vector per market.