v0.18.0

Breaking changes

• Posteriors saved under sbi v0.17.2 or older can not be loaded under sbi
v0.18.0 or newer.
• sample_with can no longer be passed to .sample(). Instead, the user has to rerun
  .build_posterior(sample_with=...). (#573)
• the posterior no longer has the the method .sample_conditional(). Using this
  feature now requires using the sampler interface (see tutorial
  here) (#573)
• retrain_from_scratch_each_round is now called retrain_from_scratch (#598, thanks to @jnsbck)
• API changes that had been introduced in sbi v0.14.0 and v0.15.0 are not enforced. Using the interface prior to
  those changes leads to an error (#645)
• prior passed to SNPE / SNLE / SNRE must be a PyTorch distribution (#655), see FAQ-7 for how to pass use custom prior.

Major changes and bug fixes

• new sampler interface (#573)
• posterior quality assurance with simulation-based calibration (SBC) (#501)
• added Sequential Neural Variational Inference (SNVI) (Glöckler et al. 2022) (#609, thanks to @manuelgloeckler)
• bugfix for SNPE-C with mixture density networks (#573)
• bugfix for sampling-importance resampling (SIR) as init_strategy for MCMC (#646)
• new density estimator for neural likelihood estimation with mixed data types (MNLE, #638)
• MCMC can now be parallelized across CPUs (#648)
• improved device check to remove several GPU issues (#610, thanks to @LouisRouillard)

Enhancements

• pairplot takes ax and fig (#557)
• bugfix for rejection sampling (#561)
• remove warninig when using multiple transforms with NSF in single dimension (#537)
• Sampling-importance-resampling (SIR) is now the default init_strategy for MCMC (#605)
• change mp_context to allow for multi-chain pyro samplers (#608, thanks to @sethaxen)
• tutorial on posterior predictive checks (#592, thanks to @LouisRouillard)
• add FAQ entry for using a custom prior (#595, thanks to @jnsbck)
• add methods to plot tensorboard data (#593, thanks to @lappalainenj)
• add option to pass the support for custom priors (#602)
• plotting method for 1D marginals (#600, thanks to @GuyMoss)
• fix GPU issues for conditional_pairplot and ActiveSubspace (#613)
• MCMC can be performed in unconstrained space also when using a MultipleIndependent distribution as prior (#619)
• added z-scoring option for structured data (#597, thanks to @rdgao)
• refactor c2st; change its default classifier to random forest (#503, thanks to @psteinb)
• MCMC init_strategy is now called proposal instead of prior (#602)
• inference objects can be serialized with pickle (#617)
• preconfigured fully connected embedding net (#644, thanks to @JuliaLinhart #624)
• posterior ensembles (#612, thanks to @jnsbck)
• remove gradients before returning the posterior (#631, thanks to @tomMoral)
• reduce batchsize of rejection sampling if few samples are left (#631, thanks to @tomMoral)
• tutorial for how to use SBC (#629, thanks to @psteinb)
• tutorial for how to use SBI with trial-based data and mixed data types (#638)
• allow to use a RestrictedPrior as prior for SNPE (#642)