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Active learning tutorial (#63)

* Create notebook for active learning example

* Overall structure for active learning notebook

* Create synthetic dataset + model for active learning example

* Plot prediction uncertainty and optimal next datapoint

* Experimented with some hyperparameters to see effects on posterior variance

* Refactored code for active learning into clear steps as functions with docstrings

* Implement active learning loop

* Tried aimplementing animation for active learning visualization

* Ruff formatting

* Fix animation displaying in notebook

* Add uncertainty subplot in animation

* Move plotting functions to plotting.py

* Compare trained model to model with passive learning

* Ruff formatting

* Add dropdown for selection of passive sampling technique

* Ensure that the passive model is trained with same number of datapoints and epochs

* Some small fixes

* Make plot ylims configurable

* Add Dropdown for curvature approximation method

* Add markdown descriptions to notebook

* Ruff formatting

* Make model for active learning tutorial smaller

* Calibrate prior precision

* Create plotting function that plots relative to men prediction

* Use new plot in animation

* Make plotting function more variable

* Ruff formatting

* Add notebook to mkdocs.yml

* Cleaned up where to split model function and parameters for laplax functions

* Made calibrate_prior_precision function depend on precomputed prob_predictive and enabled config of grid parameters

* Precompute posterior fn for uncertainty calculation and precision calibration

* Remove obsolete plotting functions

* Remove cell outputs from git

* Add mean prediction to plots

* Plot uncertainty before and after calibration

* Add markdown chapter headers to notebook

* Add markdown chapter headers to notebook

* Add nbstripout pre-commit hook

* Add a word to typos pre-commit hook whitelist

* Recalibrate after every active learning round

* Write function that returns... (continued)

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