Fix bug in importance sampled posterior

[max-dax]

What does this implement/fix? Explain your changes

When running importance sampling with sir, ImportanceSamplingPosterior._sir_sample used the wrong kwarg for oversampling_factor when calling the sir function, such that this function always ran with a default oversampling_factor of 32.

Does this close any currently open issues?

Fixes # (issue)

Any relevant code examples, logs, error output, etc?

...

Any other comments?

...

Checklist

Put an x in the boxes that apply. You can also fill these out after creating
the PR. If you're unsure about any of them, don't hesitate to ask. We're here to
help! This is simply a reminder of what we are going to look for before merging
your code.

• I have read and understood the contribution
  guidelines
• I agree with re-licensing my contribution from AGPLv3 to Apache-2.0.
• I have commented my code, particularly in hard-to-understand areas
• I have added tests that prove my fix is effective or that my feature works => see Importance sampling tutorial #1080
• I have reported how long the new tests run and potentially marked them
  with pytest.mark.slow.
• New and existing unit tests pass locally with my changes
• I performed linting and formatting as described in the contribution
  guidelines
• I rebased on main (or there are no conflicts with main)

[janfb]

Thanks for fixing this 🙏
This PR also contains the tutorial. Shall we close #1080 then and do both in this PR?

[janfb]

👍

[max-dax]

This PR also contains the tutorial. Shall we close #1080 then and do both in this PR?

Yes, I have close #1080.

[michaeldeistler]

Thanks @max-dax!