Closed ricardoV94 closed 4 months ago
Here is one manually curated model idea:
intercept_sd ~ TruncatedNormal(0.2, 0.5, 0, inf) :()
intercept ~ ZeroSumNormal(intercept_sd)) :(description,)
log_price_effect ~ LogNormal(0, 1) :()
volume_effect ~ Normal(0, 1) :()
price_volume_effect ~ Normal(0, 1) :()
manufacturer_importance::raw ~ Beta(1, 10) :(manufacturer*,)
segment_importance::raw ~ Beta(1, 10) :(manufacturer*, segment*)
no_choice_utility ~ Normal(4, 0.75) :()
market_size ~ TruncatedGamma(...) :()
total_sales_k ~ Exponential(1e+03) :()
sales_units_k ~ InverseGamma(6, 2e+04) :()
base_utility ~ f(price_volume_effect, volume_effect, log_price_effect, intercept) :(date, item)
utility ~ f(base_utility) :(date, item)
manufacturer_importance ~ f(manufacturer_importance::raw) :(manufacturer,)
segment_importance ~ f(segment_importance::raw, manufacturer_importance::raw) :(manufacturer, segment)
market_share ~ f(manufacturer_importance::raw, segment_importance::raw, utility) :(date, item)
total_sales_mean ~ f(market_size, no_choice_utility, utility), :(date)
total_sales ~ NegativeBinomial(total_sales_k, f(total_sales_mean, total_sales_k)) :(date)
sales_units ~ DirichletMN(f(total_sales), f(market_share)) :(date, item)
The three sections are: free_RVs, Deterministics, observed_RVs
Description
Currently, there is no information about dimensionality of variables:
Would be nice if output was something like the following:
1.
2.
3.
Do you like any of these. Better suggestions?