Archive for non-parametrics

fast ε-free ABC

Posted in Books, Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , on June 8, 2017 by xi'an

Last Fall, George Papamakarios and Iain Murray from Edinburgh arXived an ABC paper on fast ε-free inference on simulation models with Bayesian conditional density estimation, paper that I missed. The idea there is to approximate the posterior density by maximising the likelihood associated with a parameterised family of distributions on θ, conditional on the associated x. The data being then the ABC reference table. The family chosen there is a mixture of K Gaussian components, which parameters are then estimated by a (Bayesian) neural network using x as input and θ as output. The parameter values are simulated from an adaptive proposal that aims at approximating the posterior better and better. As in population Monte Carlo, actually. Except for the neural network part, which I fail to understand why it makes a significant improvement when compared with EM solutions. The overall difficulty with this approach is that I do not see a way out of the curse of dimensionality: when the dimension of θ increases, the approximation to the posterior distribution of θ does deteriorate, even in the best of cases, as any other non-parametric resolution. It would have been of (further) interest to see a comparison with a most rudimentary approach, namely the one we proposed based on empirical likelihoods.

empirical Bayes, reference priors, entropy & EM

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , , , , , , on January 9, 2017 by xi'an

Klebanov and co-authors from Berlin arXived this paper a few weeks ago and it took me a quiet evening in Darjeeling to read it. It starts with the premises that led Robbins to introduce empirical Bayes in 1956 (although the paper does not appear in the references), where repeated experiments with different parameters are run. Except that it turns non-parametric in estimating the prior. And to avoid resorting to the non-parametric MLE, which is the empirical distribution, it adds a smoothness penalty function to the picture. (Warning: I am not a big fan of non-parametric MLE!) The idea seems to have been Good’s, who acknowledged using the entropy as penalty is missing in terms of reparameterisation invariance. Hence the authors suggest instead to use as penalty function on the prior a joint relative entropy on both the parameter and the prior, which amounts to the average of the Kullback-Leibler divergence between the sampling distribution and the predictive based on the prior. Which is then independent of the parameterisation. And of the dominating measure. This is the only tangible connection with reference priors found in the paper.

The authors then introduce a non-parametric EM algorithm, where the unknown prior becomes the “parameter” and the M step means optimising an entropy in terms of this prior. With an infinite amount of data, the true prior (meaning the overall distribution of the genuine parameters in this repeated experiment framework) is a fixed point of the algorithm. However, it seems that the only way it can be implemented is via discretisation of the parameter space, which opens a whole Pandora box of issues, from discretisation size to dimensionality problems. And to motivating the approach by regularisation arguments, since the final product remains an atomic distribution.

While the alternative of estimating the marginal density of the data by kernels and then aiming at the closest entropy prior is discussed, I find it surprising that the paper does not consider the rather natural of setting a prior on the prior, e.g. via Dirichlet processes.

Peter Hall (1951-2016)

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , , , , , on January 10, 2016 by xi'an

I just heard that Peter Hall passed away yesterday in Melbourne. Very sad news from down under. Besides being a giant in the fields of statistics and probability, with an astounding publication record, Peter was also a wonderful man and so very much involved in running local, national and international societies. His contributions to the field and the profession are innumerable and his loss impacts the entire community. Peter was a regular visitor at Glasgow University in the 1990s and I crossed paths with  him a few times, appreciating his kindness as well as his highest dedication to research. In addition, he was a gifted photographer and I recall that the [now closed] wonderful guest-house where we used to stay at the top of Hillhead had a few pictures of his taken in the Highlands and framed on its walls. (If I remember well, there were also beautiful pictures of the Belgian countryside by him at CORE, in Louvain-la-Neuve.) I think the last time we met was in Melbourne, three years ago… Farewell, Peter, you certainly left an indelible print on a lot of us.

[Song Chen from Beijing University has created a memorial webpage for Peter Hall to express condolences and share memories.]

Inference for stochastic simulation models by ABC

Posted in Books, Statistics, University life with tags , , , , , on February 13, 2015 by xi'an

Hartig et al. published a while ago (2011) a paper  in Ecology Letters entitled “Statistical inference for stochastic simulation models – theory and application”, which is mostly about ABC. (Florian Hartig pointed out the paper to me in a recent blog comment. about my discussion of the early parts of Guttman and Corander’s paper.) The paper is largely a tutorial and it reminds the reader about related methods like indirect inference and methods of moments. The authors also insist on presenting ABC as a particular case of likelihood approximation, whether non-parametric or parametric. Making connections with pseudo-likelihood and pseudo-marginal approaches. And including a discussion of the possible misfit of the assumed model, handled by an external error model. And also introducing the notion of informal likelihood (which could have been nicely linked with empirical likelihood). A last class of approximations presented therein is called rejection filters and reminds me very much of Ollie Ratman’s papers.

“Our general aim is to find sufficient statistics that are as close to minimal sufficiency as possible.” (p.819)

As in other ABC papers, and as often reported on this blog, I find the stress on sufficiency a wee bit too heavy as those models calling for approximation almost invariably do not allow for any form of useful sufficiency. Hence the mathematical statistics notion of sufficiency is mostly useless in such settings.

“A basic requirement is that the expectation value of the point-wise approximation of p(Sobs|φ) must be unbiased” (p.823)

As stated above the paper is mostly in tutorial mode, for instance explaining what MCMC and SMC methods are. As illustrated by the above figure. There is however a final and interesting discussion section on the impact of estimating the likelihood function at different values of the parameter. However, the authors seem to focus solely on pseudo-marginal results to validate this approximation, hence on unbiasedness, which does not work for most ABC approaches that I know. And for the approximations listed in the survey. Actually, it would be quite beneficial to devise a cheap tool to assess the bias or extra-variation due to the use of approximative techniques like ABC… A sort of 21st Century bootstrap?!

Bayesian optimization for likelihood-free inference of simulator-based statistical models

Posted in Books, Statistics, University life with tags , , , , on January 29, 2015 by xi'an

Michael Gutmann and Jukka Corander arXived this paper two weeks ago. I read part of it (mostly the extended introduction part) on the flight from Edinburgh to Birmingham this morning. I find the reflection it contains on the nature of the ABC approximation quite deep and thought-provoking.  Indeed, the major theme of the paper is to visualise ABC (which is admittedly shorter than “likelihood-free inference of simulator-based statistical models”!) as a regular computational method based on an approximation of the likelihood function at the observed value, yobs. This includes for example Simon Wood’s synthetic likelihood (who incidentally gave a talk on his method while I was in Oxford). As well as non-parametric versions. In both cases, the approximations are based on repeated simulations of pseudo-datasets for a given value of the parameter θ, either to produce an estimation of the mean and covariance of the sampling model as a function of θ or to construct genuine estimates of the likelihood function. As assumed by the authors, this calls for a small dimension θ. This approach actually allows for the inclusion of the synthetic approach as a lower bound on a non-parametric version.

In the case of Wood’s synthetic likelihood, two questions came to me:

  • the estimation of the mean and covariance functions is usually not smooth because new simulations are required for each new value of θ. I wonder how frequent is the case where we can always use the same basic random variates for all values of θ. Because it would then give a smooth version of the above. In the other cases, provided the dimension is manageable, a Gaussian process could be first fitted before using the approximation. Or any other form of regularization.
  • no mention is made [in the current paper] of the impact of the parametrization of the summary statistics. Once again, a Cox transform could be applied to each component of the summary for a better proximity of/to the normal distribution.

When reading about a non-parametric approximation to the likelihood (based on the summaries), the questions I scribbled on the paper were:

  • estimating a complete density when using this estimate at the single point yobs could possibly be superseded by a more efficient approach.
  • the authors study a kernel that is a function of the difference or distance between the summaries and which is maximal at zero. This is indeed rather frequent in the ABC literature, but does it impact the convergence properties of the kernel estimator?
  • the estimation of the tolerance, which happens to be a bandwidth in that case, does not appear to be processed in this paper, which could explain for very low probabilities of acceptance mentioned in the paper.
  • I am lost as to why lower bounds on likelihoods are relevant here. Unless this is intended for ABC maximum likelihood estimation.

Guttmann and Corander also comment on the first point, through the cost of producing a likelihood estimator. They therefore suggest to resort to regression and to avoid regions of low estimated likelihood. And rely on Bayesian optimisation. (Hopefully to be commented later.)

rate of convergence for ABC

Posted in Statistics, University life with tags , , , , on November 19, 2013 by xi'an

Barber, Voss, and Webster recently posted and arXived a paper entitled The Rate of Convergence for Approximate Bayesian Computation. The paper is essentially theoretical and establishes the optimal rate of convergence of the MSE—for approximating a posterior moment—at a rate of 2/(q+4), where q is the dimension of the summary statistic, associated with an optimal tolerance in n-1/4. I was first surprised at the role of the dimension of the summary statistic, but rationalised it as being the dimension where the non-parametric estimation takes place. I may have read the paper too quickly as I did not spot any link with earlier convergence results found in the literature: for instance, Blum (2010, JASA) links ABC with standard kernel density non-parametric estimation and find a tolerance (bandwidth) of order n-1/q+4 and an MSE of order 2/(q+4) as well. Similarly, Biau et al. (2013, Annales de l’IHP) obtain precise convergence rates for ABC interpreted as a k-nearest-neighbour estimator. And, as already discussed at length on this blog, Fearnhead and Prangle (2012, JRSS Series B) derive rates similar to Blum’s with a tolerance of order n-1/q+4 for the regular ABC and of order n-1/q+2 for the noisy ABC

Le Monde rank test (cont’d)

Posted in R, Statistics with tags , , , on April 5, 2010 by xi'an

Following a comment from efrique pointing out that this statistic is called Spearman footrule, I want to clarify the notation in

\mathfrak{M}_n = \sum_{i=1}^n |r^x_i-r^y_i|\,,

namely (a) that the ranks of x_i and y_i are considered for the whole sample, i.e.

\{r^x_1,\ldots,r^x_n,r^y_1,\ldots,r^y_n\} = \{1,\ldots,2n\}

instead of being computed separately for the x‘s and the y‘s, and then (b) that the ranks are reordered for each group (meaning that the groups could be of different sizes). This statistics is therefore different from the Spearman footrule studied by Persi Diaconis and R. Graham in a 1977 JRSS paper,

\mathfrak{D}_ n = \sum_{i=1}^n |\pi(i)-\sigma(i)|\,,

where \pi and \sigma are permutations from \mathfrak{S}_n. The mean of \mathfrak{D}_ n is approximately n^{2/3}. I mistakenly referred to Spearman’s ρ rank correlation test in the previous post. It is actually much more related to the Siegel-Tukey test, even though I think there exists a non-parametric test of iid-ness for paired observations… The x‘s and the y‘s are thus not paired, despite what I wrote previously. This distance must be related to some non-parametric test for checking the equality of location parameters.