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I have just released version 0.3 of particles (my Python Sequential Monte Carlo library). Here are the main changes:

No more Fortran dependency

Previous versions of particles relied on a bit of Fortran code to produce QMC (quasi-Monte Carlo) points. This code was automatically compiled during the installation. This was working fine for most users, but not all, unfortunately.

The latest (1.7) version of Scipy includes a stats.qmc sub-module. Particles 0.3 relies on this sub-module to generate QMC points, and thus is a pure Python package. This should mean fewer headaches when installing particles. Please let me know if this new version is indeed easier to install for you. Of course, make sure you have updated Scipy before installing particles; e.g. conda update scipy if you are using conda.

Waste-free SMC

With Dang, we wrote a paper on a new class of SMC samplers, waste-free SMC; see this paper on arxiv (to be published soon in JRSSB). In particular, the new version describes a particular scenario where it is possible to show formally that waste-free SMC >> standard SMC (in the sense of lower asymptotic variance).

The module smc_samplers now implements waste-free SMC by default (but standard SMC is still available, through option wastefree=False). Check the following notebook to see how to run an SMC sampler in particles.

SMC samplers for binary spaces

The new module binary_smc implements SMC samplers for binary spaces, i.e. {0, 1}^d, following Chopin and Schäfer (2014).

papers folder

The package now includes a folder called “papers”, which contains scripts that reproduce selected numerical experiments from previous papers:

• scripts in sub-folder binarySMC reproduce most of the numerical experiments from Schäfer and Chopin (2014).
• a script in sub-folder wastefreeSMC reproduces the first numerical experiment of Dau & Chopin (2020) on logistic regression. (See Dang’s github repo for the other experiments.)

Misc

• Added a new resampling scheme, called killing (which may be traced to papers and work by Pierre del Moral).
• Added a tutorial notebook on how to define non-trivial state-space models.

Comments / questions / poems ?

If you want to try particles, the first thing to read is the notebook tutorials. Second thing is to read the documentation of the respective modules. If you are still lost, feel free to raise an issue on github (or send me an e-mail, but github issues are more practical).

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