Nicolas Chopin

Sorry, I completely missed this. I guess it was during my vacations this summer. I will look into it.

Hi, The proposal distribution in ``mcmc.pmmh`` is a Gaussian random walk; i.e. the proposed parameter value is simulated from $\theta^p | \theta \sim N(\theta, \Sigma)$. The proposal covariance $\Sigma$ is...

First, to set the covariance matrix of the proposal, use argument `rw_cov`; see here: https://particles-sequential-monte-carlo-in-python.readthedocs.io/en/latest/_autosummary/particles.mcmc.PMMH.html#particles.mcmc.PMMH But now that you mention it, I can see that the docstring is not super-clear......

The paper we wrote with @hai-dang-dau on waste-free SMC ([here](https://arxiv.org/pdf/2011.02328.pdf)) contains 3 numerical experiments; in each experiment, waste-free outperforms vanilla SMC. I particularly like the one on counting Latin squares....

Well, I'm happy that you found the book useful! :-) So what you're saying by "more general" than a "mere sampler" is that you want to be able to approximate...

Hi, yes, you're right, all the HMMs considered in the book etc are without control inputs. One quick and dirty way to deal with your issue is to define the...

More details are now given in the docstrings of PMMH and parent classes regarding parameter `rw_cov`, and more generally how to calibrate random walk proposals.

1. Attribute X is typically a numpy array of shape N or (N, d), which contains your N particles; these particles N particles are associated with N weights, which are...