Sbi

Latest version: v0.23.3

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0.14.2

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

0.14.1

- 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)

0.14.0

- 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.

0.13.2

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

0.13.1

- Make logging of vectorized numpy slice sampler slightly less verbose and address NumPy future warning (347)
- Allow continuation of MCMC chains (348)

0.13.0

- Conditional distributions and correlations for analysing the posterior (321)
- Moved rarely used arguments from pairplot into kwargs (321)
- Sampling from conditional posterior (327)
- Allow inference with multi-dimensional x when appropriate embedding is passed (335)
- Fixes a bug with clamp_and_warn not overriding num_atoms for SNRE and the warning message itself (338)
- Compatibility with Pyro 1.4.0 (339)
- Speed up posterior rejection sampling by introducing batch size (340, 343)
- Allow vectorized evaluation of numpy potentials (341)
- Adds vectorized version of numpy slice sampler which allows parallel log prob evaluations across all chains (344)

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