BlackJAX v0.6.0

What's Changed

• Specify custom gradients. by @FedericoV in #205
• Add Elliptical slice sampler by @albcab in #183

New Contributors

• @FedericoV made their first contribution in #205

Full Changelog: 0.5.0...0.6.0

BlackJAX v0.5.0

What's Changed

• Fix tests and pre-commits on local devices by @albcab in #188
• Add the MALA sampling algorithm by @rlouf in #189
• Fix install instructions in README by @FredericWantiez in #196
• Adding progress bars to window adaptation by @zaxtax in #190
• Add the Orbital HMC sampler by @albcab

New Contributors

• @FredericWantiez made their first contribution in #196

BlackJAX 0.4.0

Breaking changes

⚠️ This release changes the high-level API as well as import paths ⚠️

This release simplifies the high-level API for samplers. For instance, to initialize and use a HMC kernel:

import blackjax

hmc = blackjax.hmc(logprob_fn step_size, inverse_mass_matrix, num_integration_steps)
state = hmc.init(position)
new_state, info = hmc.step(rng_key, state)

hmc is now a namedtuple with a init and a step function; you only need to pass logprob_fn at initialization unlike the previous version. The internals were simplified a lot, and the hierarchy is now more flat. For instance, to use the base HMC kernel directly:

import blackjax.mcmc.integrators as integrators
import blackjax.mcmc.hmc as hmc

kernel = hmc.kernel(integrators.mclachlan)
state = hmc.init(position, logprob_fn)
state, info = kernel(rng_key, state, logprob_fn, step_size, inverse_mass_matrix, num_integration_steps)

The API of the base kernels has also been changed to be more flexible.

Performance improvements

Thanks to the work of @zaxtax @junpenglao and @rlouf the performance of the NUTS sampler (especially the warmup) has been greatly improved and is now at least on par with numpyro.

What's Changed

No new algorithm in this release, but important work was done on the API, the internals and the examples.

• Fix SMC notebook as per issue #148 by @AdrienCorenflos in #149
• Add a logistic regression example by @gerdm in #103
• Update to fold in all trajectory building into a single while_loop by @junpenglao in #164
• Update use_with_numpyro.ipynb by @junpenglao in #165
• Updated trajectory.py by @Gautam-Hegde in #167
• Simplify the user API & create a more general kernel by @rlouf in #159
• Make the PyMC example run on version 4 by @rlouf in #178
• Moved dual_averaging.py to /adaptation and renamed it to optimizers.py by @Bnux256 in #179
• Fix example notebook to use pymc 4 by @zaxtax in #180

New Contributors

• @gerdm made their first contribution in #103
• @Gautam-Hegde made their first contribution in #167
• @Bnux256 made their first contribution in #179
• @zaxtax made their first contribution in #180

Full Changelog: 0.3.0...0.4.0

BlackJAX 0.3.0

What changed

Breaking changes

To build a HMC or NUTS kernel in 0.2.1 and previous versions one needed to provide a potential_fn function:

kernel = nuts.kernel(potential_fn, step_size, inverse_mass_matrix)

Instead we now ask the users to provide the more commonly used log-probability function:

kernel = nuts.kernel(logprob_fn, step_size, inverse_mass_matrix)

where logprob_fn = lambda x: -potential_fn(*x)

New features

• Tempered Sequential Monte Carlo (@AdrienCorenflos #40 )
• Rosenbluth Metropolis Hastings algorithm (@AdrienCorenflos #74 )
• Effective Sample Size, RHat (@junpenglao #66 )
• Higher-order integrators for HMC (@rlouf #59 )

Bugs

• Missing key splitting in trajectory integration (@wiep #53 )

BlackJAX 0.2.1

What changed

• momentum and position were passed to the kinetic energy in the wrong order, leading to biased sampling as noticed in #46. We corrected this behavior and added a new test.

BlackJAX 0.2

What changed

• The Stan adaptation scheme, including dual averaging, computing covariance with Welford's algorithm and the schedule (@rlouf)
• Recursive implementation of NUTS (@junpenglao)
• Many BUG fixes on NUTS (@junpenglao)

BlackJAX 0.1

New features

• hmc kernel
• nuts kernel
• Notebook with examples of how to sample one or multiple chains with HMC, NUTS