Sbi

Major changes

- Active subspaces for sensitivity analysis (394, [tutorial](https://sbi-dev.github.io/sbi/tutorial/09_sensitivity_analysis/))
- Method to compute the maximum-a-posteriori estimate from the posterior (412)

API changes

- `pairplot()`, `conditional_pairplot()`, and `conditional_corrcoeff()` should now be imported from `sbi.analysis` instead of `sbi.utils` (394).
- Changed `fig_size` to `figsize` in pairplot (394).
- moved `user_input_checks` to `sbi.utils` (430).

Minor changes

- Depend on new `joblib=1.0.0` and fix progress bar updates for multiprocessing (421).
- Fix for embedding nets with `SNRE` (thanks adittmann, 425).
- Is it now optional to pass a prior distribution when using SNPE (426).
- Support loading of posteriors saved after `sbi v0.15.0` (427, thanks psteinb).
- Neural network training can be resumed (431).
- Allow using NSF to estimate 1D distributions (438).
- Fix type checks in input checks (thanks psteinb, 439).
- Bugfix for GPU training with SNRE_A (thanks glouppe, 442).

- Fixup for conditional correlation matrix (thanks JBeckUniTb, 404)
- z-score data using only the training data (411)

- Small fix for SMC-ABC with semi-automatic summary statistics (402)

- Support for training and sampling on GPU including fixes from `nflows` (331)
- Bug fix for SNPE with neural spline flow and MCMC (398)
- Small fix for SMC-ABC particles covariance
- Small fix for rejection-classifier (396)

- New flexible interface API (378). This is going to be a breaking change for users of
the flexible interface and you will have to change your code. Old syntax:

python
from sbi.inference import SNPE, prepare_for_sbi

simulator, prior = prepare_for_sbi(simulator, prior)
inference = SNPE(simulator, prior)

Simulate, train, and build posterior.
posterior = inference(num_simulation=1000)


New syntax:

python
from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi

simulator, prior = prepare_for_sbi(simulator, prior)
inference = SNPE(prior)

theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000)
density_estimator = inference.append_simulations(theta, x).train()
posterior = inference.build_posterior(density_estimator) MCMC kwargs go here.


More information can be found here [here](https://sbi-dev.github.io/sbi/tutorial/02_flexible_interface/).

- Fixed typo in docs for `infer` (thanks glouppe, 370)
- New `RestrictionEstimator` to learn regions of bad simulation outputs (390)
- Improvements for and new ABC methods (395)
- Linear regression adjustment as in Beaumont et al. 2002 for both MCABC and SMCABC
- Semi-automatic summary statistics as in Fearnhead & Prangle 2012 for both MCABC and SMCABC
- Small fixes to perturbation kernel covariance estimation in SMCABC.

- Fix bug in SNRE (363)
- Fix warnings for multi-D x (361)
- Small improvements to MCMC, verbosity and continuing of chains (347, 348)