Becks Simpson

1. Sounds like a good use case for media channel specific transform priors. The default is `dist.Beta(concentration1=2., concentration0=1.)`, this assumes most-probably adstock is 1, although this is relatively weak. I...

@BrianMiner yes. The beta's you are referring to, are these the beta coefficients that are multiplied to the adstocked-saturated media data? The beta coefficients that represent ~roughly the effectiveness, or...

@BrianMiner Yes, the `media_prior` is how LightweightMMM supports this out of box. They give the example of using 1-mean scaled spend %'s. This package only learns a fixed distribution for...

The expected transformations of the media data, partially depends on the saturation function. Hill saturation I believe maps input to 0 --> 1 range (used in hill_adstock model). While exponent...

@BrianMiner Sorry for the delay in response and confusion: 1. Expected_ROI_£_from_imp_from_exps. This would be your ROI, £ for example, of your target KPI, for a given number of impressions, you'd...

1. Here 0.15 is what they used in their notebook `simple_end_to_end_demo.ipynb` to "reflect typical MMM ranges", I've actually never been able to determine where they get their number from. ```cost_scaler...

@cincysam6 A lot of the time I just give it an eyeball. As it's using a pretty encapsulated function from pyro, I think the simplest, beyond screen shoting is you...

As the "cost" for zero-cost channels, for instance organic, I have passed in numbers such as "total visitors" in the past, essentially in LightweightMMM the media data itself. Then it...

@Aanai You can, they would not be modelled with saturation effects or adstock, just with a linear multiplier. That is the only tradeoff. If you find that they learn negative...

@Aanai I think you can accomplish this with with a `TruncateNormalDistribution` put into custom_priors. Say your third extra feature is strictly non-zero. ``` custom_priors = { _COEF_EXTRA_FEATURES: numpyro.distributions.TruncatedNormalDistribution( loc =...