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Description
Description
To be honest I'm a little unsure here, but in 5.10
with pm.Model() as sp_individual:
τ = pm.HalfCauchy("τ", 1)
β = pm.Normal("β", mu=0, sigma=τ, shape=(Bi.shape[1], groups))
μi = pm.Deterministic("μi", pm.math.dot(Bi, β))
σ = pm.HalfNormal("σ", 1)
up = pm.Normal("up", μi[:,index].T.ravel(), σ, observed=uptake)
idata_sp_individual = pm.sample(2000, return_inferencedata=True)
where
index = range(12)
groups = len(index)
I'm a bit confused on this line:
up = pm.Normal("up", μi[:,index].T.ravel(), σ, observed=uptake)
specifically the μi[:, index]
statement. If groups = len(index)
, μi
has len(index)
columns. So if we slice it using μi[:, index]
, then I think we do nothing to it? we're essentially saying: Take all rows (:
) and all columns (index
since μi
has len(index)
rows) of μi. So μi[:, index] == μi
, and the slicing does nothing.
Reference
Author Section
Do not close until
- Added to Errata
- Fixed in Open Access
- Fixed in latex source