Archive for forward-backward formula

Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , on March 17, 2016 by xi'an

IMG_19390Previously, I posted a comment on a paper by Alex Shestopaloff and Radford Neal, after my visit to Toronto two years ago, using a particular version of ensemble Monte Carlo. A new paper by the same authors was recently arXived, as an refinement of the embedded HMM paper of Neal (2003), in that the authors propose a new and more efficient way to generate from the (artificial) embedded hidden Markov sampler that is central to their technique of propagating a set of pool states. The method exploits both forward and backward representations of HMMs in an alternating manner. And propagates the pool states from one observation time to the next. The paper also exploits latent Gaussian structures to make autoregressive proposals, as well as flip proposals from x to -x [which seem to only make sense when 0 is a central value for the target, i.e. when the observables y only depend on |x|]. All those modifications bring the proposal quite close to (backward) particle Gibbs, the difference being in using Metropolis rather than importance steps. And in an improvement brought by the embedded HMM approach, even though it is always delicate to generalise those comparisons when some amount of calibration is required by both algorithms under comparison. (Especially delicate when it is rather remote from my area of expertise!) Anyway, I am still intrigued [in a positive way] by the embedded HMM idea as it remains mysterious that a finite length HMM simulation can improve the convergence performances that much. And wonder at a potential connection with an earlier paper of Anthony Lee and Krys Latuszynski using a random number of auxiliary variables. Presumably a wrong impression from a superficial memory…

perfect sampling, just perfect!

Posted in Books, Statistics, University life with tags , , , , , , , , on January 19, 2016 by xi'an

Great news! Mark Huber (whom I’ve know for many years, so this review may be not completely objective!) has just written a book on perfect simulation! I remember (and still share) the excitement of the MCMC community when the first perfect simulation papers of Propp and Wilson (1995) came up on the (now deceased) MCMC preprint server, as it seemed then the ideal (perfect!) answer to critics of the MCMC methodology, plugging MCMC algorithms into a generic algorithm that eliminating burnin, warmup, and convergence issues… It seemed both magical, with the simplest argument: “start at T=-∞ to reach stationarity at T=0”, and esoteric (“why forward fails while backward works?!”), requiring simple random walk examples (and a java app by Jeff Rosenthal) to understand the difference (between backward and forward), as well as Wilfrid Kendall’s kids’ coloured wood cubes and his layer of leaves falling on the ground and seen from below… These were exciting years, with MCMC still in its infancy, and no goal seemed too far away! Now that years have gone, and that the excitement has clearly died away, perfect sampling can be considered in a more sedate manner, with pros and cons well-understood. This is why Mark Huber’s book is coming at a perfect time if any! It covers the evolution of the perfect sampling techniques, from the early coupling from the past to the monotonous versions, to the coalescence principles, with applications to spatial processes, to the variations on nested sampling and their use in doubly intractable distributions, with forays into the (fabulous) Bernoulli factory problem (a surprise for me, as Bernoulli factories are connected with unbiasedness, not stationarity! Even though my only fieldwork [with Randal Douc] in such factories was addressing a way to turn MCMC into importance sampling. The key is in the notion of approximate densities, introduced in Section 2.6.). The book is quite thorough with the probabilistic foundations of the different principles, with even “a [tiny weeny] little bit of measure theory.

Any imperfection?! Rather, only a (short too short!) reflection on the limitations of perfect sampling, namely that it cannot cover the simulation of posterior distributions in the Bayesian processing of most statistical models. Which makes the quote

“Distributions where the label of a node only depends on immediate neighbors, and where there is a chance of being able to ignore the neighbors are the most easily handled by perfect simulation protocols (…) Statistical models in particular tend to fall into this category, as they often do not wish to restrict the outcome too severely, instead giving the data a chance to show where the model is incomplete or incorrect.” (p.223)

just surprising, given the very small percentage of statistical models which can be handled by perfect sampling. And the downsizing of perfect sampling related papers in the early 2000’s. Which also makes the final and short section on the future of perfect sampling somewhat restricted in its scope.

So, great indeed!, a close to perfect entry to a decade of work on perfect sampling. If you have not heard of the concept before, consider yourself lucky to be offered such a gentle guidance into it. If you have dabbled with perfect sampling before, reading the book will be like meeting old friends and hearing about their latest deeds. More formally, Mark Huber’s book should bring you a new perspective on the topic. (As for me, I had never thought of connecting perfect sampling with accept reject algorithms.)

Quasi-Monte Carlo sampling

Posted in Books, Kids, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , on December 10, 2014 by xi'an

RSS wine“The QMC algorithm forces us to write any simulation as an explicit function of uniform samples.” (p.8)

As posted a few days ago, Mathieu Gerber and Nicolas Chopin will read this afternoon a Paper to the Royal Statistical Society on their sequential quasi-Monte Carlo sampling paper.  Here are some comments on the paper that are preliminaries to my written discussion (to be sent before the slightly awkward deadline of Jan 2, 2015).

Quasi-Monte Carlo methods are definitely not popular within the (mainstream) statistical community, despite regular attempts by respected researchers like Art Owen and Pierre L’Écuyer to induce more use of those methods. It is thus to be hoped that the current attempt will be more successful, it being Read to the Royal Statistical Society being a major step towards a wide diffusion. I am looking forward to the collection of discussions that will result from the incoming afternoon (and bemoan once again having to miss it!).

“It is also the resampling step that makes the introduction of QMC into SMC sampling non-trivial.” (p.3)

At a mathematical level, the fact that randomised low discrepancy sequences produce both unbiased estimators and error rates of order

\mathfrak{O}(N^{-1}\log(N)^{d-}) \text{ at cost } \mathfrak{O}(N\log(N))

means that randomised quasi-Monte Carlo methods should always be used, instead of regular Monte Carlo methods! So why is it not always used?! The difficulty stands [I think] in expressing the Monte Carlo estimators in terms of a deterministic function of a fixed number of uniforms (and possibly of past simulated values). At least this is why I never attempted at crossing the Rubicon into the quasi-Monte Carlo realm… And maybe also why the step had to appear in connection with particle filters, which can be seen as dynamic importance sampling methods and hence enjoy a local iid-ness that relates better to quasi-Monte Carlo integrators than single-chain MCMC algorithms.  For instance, each resampling step in a particle filter consists in a repeated multinomial generation, hence should have been turned into quasi-Monte Carlo ages ago. (However, rather than the basic solution drafted in Table 2, lower variance solutions like systematic and residual sampling have been proposed in the particle literature and I wonder if any of these is a special form of quasi-Monte Carlo.) In the present setting, the authors move further and apply quasi-Monte Carlo to the particles themselves. However, they still assume the deterministic transform

\mathbf{x}_t^n = \Gamma_t(\mathbf{x}_{t-1}^n,\mathbf{u}_{t}^n)

which the q-block on which I stumbled each time I contemplated quasi-Monte Carlo… So the fundamental difficulty with the whole proposal is that the generation from the Markov proposal


has to be of the above form. Is the strength of this assumption discussed anywhere in the paper? All baseline distributions there are normal. And in the case it does not easily apply, what would the gain bw in only using the second step (i.e., quasi-Monte Carlo-ing the multinomial simulation from the empirical cdf)? In a sequential setting with unknown parameters θ, the transform is modified each time θ is modified and I wonder at the impact on computing cost if the inverse cdf is not available analytically. And I presume simulating the θ’s cannot benefit from quasi-Monte Carlo improvements.

The paper obviously cannot get into every detail, obviously, but I would also welcome indications on the cost of deriving the Hilbert curve, in particular in connection with the dimension d as it has to separate all of the N particles, and on the stopping rule on m that means only Hm is used.

Another question stands with the multiplicity of low discrepancy sequences and their impact on the overall convergence. If Art Owen’s (1997) nested scrambling leads to the best rate, as implied by Theorem 7, why should we ever consider another choice?

In connection with Lemma 1 and the sequential quasi-Monte Carlo approximation of the evidence, I wonder at any possible Rao-Blackwellisation using all proposed moves rather than only those accepted. I mean, from a quasi-Monte Carlo viewpoint, is Rao-Blackwellisation easier and is it of any significant interest?

What are the computing costs and gains for forward and backward sampling? They are not discussed there. I also fail to understand the trick at the end of 4.2.1, using SQMC on a single vector instead of (t+1) of them. Again assuming inverse cdfs are available? Any connection with the Polson et al.’s particle learning literature?

Last questions: what is the (learning) effort for lazy me to move to SQMC? Any hope of stepping outside particle filtering?

MCMC for non-linear state space models using ensembles of latent sequences

Posted in Statistics with tags , , , , on November 6, 2013 by xi'an

IMG_19390While visiting U of T, I had the opportunity to discuss the above paper MCMC for non-linear state space models using ensembles of latent sequences with both authors, Alex Shestopalo ff and Radford Neal, paper that I had completely missed during my hospital break. The paper borrows from the central idea of Neal (2003), which “is to temporarily reduce the state space of the model, which may be countably in finite or continuous, to a finite collection of randomly generated `pool’ states at each time point.” Several copies of the hidden Markov chain are generated from a proposal and weighted according to the posterior. This makes for a finite state space on which a forward-backward algorithm can be run, thus producing a latent sequence that is more likely than the ones originally produced from the proposal, as it borrows at different times from different chains.  (I alas had no lasting memory of this early paper of Radford’s. But this is essentially ensemble Monte Carlo.)

What Radford patiently explained to me in Toronto was why this method did not have the same drawback as an importance sampling method, as the weights were local rather than global and hence did not degenerate as the length of the chain/HMM increased. Which I find a pretty good argument! The trick of being able to rely on forward-backward simulation is also very appealing. This of course does not mean that the method is always converging quickly, as the proposal matters. A novelty of the current paper is the inclusion of parameter simulation steps as well, steps that are part of the ensemble Monte Carlo process (rather than a standard Gibbs implementation). There also is a delayed acceptance (as opposed to delayed rejection) step where a subset of the chain is used to check for early (and cheaper) rejection.

The paper is a bit short on describing the way pool states can be generated (see Section 7), but it seems local (time-wise) perturbations of the current state are considered. I wonder if an intermediate particle step could produce a more efficient proposal… It also seems possible to consider a different number of pool states at more uncertain or more sticky times, with a potential for adaptivity depending on the acceptance rate.

For the evaluation of the method, the authors consider the Ricker population dynamics model found in Wood (2003) and Fearnhead and Prangle (2012), where semi-automated ABC is used. In the experiment described therein, there are only 100 latent states, which is enough to hinder MCMC. The ensemble method does much better. While there is no comparison with ABC, I would presume this method, relying on a more precise knowledge of the probabilistic model, should do better. Maybe a good topic for a Master project?