Model comparison with ArviZ is more easily done than said!
waic_l = az.waic(trace_l)waic_l
|
waic |
waic_se |
p_waic |
warning |
|
|
0 |
28.750381 |
5.303983 |
2.443984 |
0 |
If you want to compute LOO instead of WAIC, you must use az.loo.
For WAIC and LOO PyMC3 reports four values:
- A point estimate
- The standard error of the point estimate (this is computed by assuming normality and hence it may not be very reliable when the sample size is low)
- The effective number of parameters
- A warning (read the A note on the reliability of WAIC and LOO computations section for more details)
Since the values of WAIC/LOO are always interpreted in a relative fashion, that is, by comparing them across models, ArviZ provides ...
