I found several useful applications of pseudo-random number sampling in the past. In particular:

• Inverse transform sampling
• Gibbs sampling

(This issue serves a reminder to add the respective methods. Pull requests always welcome.)

how to plot top losses examples/images?

68:43-73:22
how to plot top losses examples/images
How to create model interpreter?
- interp = ClassificationInterpretation.from_learner(learn)
Why plot the high loss?
- to find out our high prob predictions are wrong
- they are the defect of our model
How to plot top losses using the interpre

As the current capabilities of PyNomaly are solidified and new capabilities added, it would be beneficial to have dedicated documentation that is hosted and available to users outside of the readme.

sbt experiment command in root should execute all experiments (c.f. flex.experiment package). However, for now, only one experiment is executed (with arg0). Therefore, the experiment command that does not have an argument must perform all the experiment codes. See Tasks.

near the beginning of the pdf, you claim 3 axioms without explaining where the second one came from/mentioning the symbol "S", which I assume is meant to denote sample space

Add guide in daviddalpiaz/appliedstats#79 (comment) to the front matter with a link in the lower left for "spot a typo?"

Potentially, might want to add in hypothes.is (e.g. a "suggest"/annotation layer.)

Each section of the Users Manual should have a small discussion of theory behind the method. This should be just enough to understand the inputs and outputs, without derivations (typically no more than one page, see e.g. Nataf). If the theory is complex and requires elaborate discussion, references should be provided that give adequate background to the user.

• MCS

• LHS

• STS