**G**reat news!, the ABC paper we had originally started in 2012 in Melbourne with Gael Martin and Brendan MacCabe, before joining forces with David Frazier and Worapree Maneesoothorn, in expanding its scope to using auxiliary likelihoods to run ABC in state-space models, just got accepted in the Journal of Computational and Graphical Statistics. A reason to celebrate with a Mornington Peninsula Pinot Gris wine next time I visit Monash!

## Archive for auxiliary model

## down-under ABC paper accepted in JCGS!

Posted in Books, pictures, Statistics, University life with tags ABC, Australia, auxiliary model, JCGS, journal, Journal of Computational and Graphical Statistics, Melbourne, Monash University, Mornington Peninsula, pinot gris, publication, state space model, Victoria wines on October 25, 2018 by xi'an## auxiliary likelihood-based approximate Bayesian computation in state-space models

Posted in Books, pictures, Statistics, University life with tags ABC, auxiliary model, consistency, Kalman filter, Melbourne, Monash University, score function, summary statistics on May 2, 2016 by xi'an**W**ith Gael Martin, Brendan McCabe, David T. Frazier, and Worapree Maneesoonthorn, we arXived (and submitted) a strongly revised version of our earlier paper. We begin by demonstrating that reduction to a set of *sufficient* statistics of reduced dimension relative to the sample size is infeasible for most state-space models, hence calling for the use of *partial* posteriors in such settings. Then we give conditions [like parameter identification] under which ABC methods are Bayesian consistent, when using an auxiliary model to produce summaries, either as MLEs or [more efficiently] scores. Indeed, for the order of accuracy required by the ABC perspective, scores are equivalent to MLEs but are computed much faster than MLEs. Those conditions happen to to be weaker than those found in the recent papers of Li and Fearnhead (2016) and Creel et al. (2015). In particular as we make no assumption about the limiting distributions of the summary statistics. We also tackle the dimensionality curse that plagues ABC techniques by numerically exhibiting the improved accuracy brought by looking at marginal rather than joint modes. That is, by matching individual parameters via the corresponding scalar score of the *integrated* auxiliary likelihood rather than matching on the multi-dimensional score statistics. The approach is illustrated on realistically complex models, namely a (latent) Ornstein-Ulenbeck process with a discrete time linear Gaussian approximation is adopted and a Kalman filter auxiliary likelihood. And a square root volatility process with an auxiliary likelihood associated with a Euler discretisation and the augmented unscented Kalman filter. In our experiments, we compared our auxiliary based technique to the two-step approach of Fearnhead and Prangle (in the Read Paper of 2012), exhibiting improvement for the examples analysed therein. Somewhat predictably, an important challenge in this approach that is common with the related techniques of indirect inference and efficient methods of moments, is the choice of a computationally efficient and accurate auxiliary model. But most of the current ABC literature discusses the role and choice of the summary statistics, which amounts to the same challenge, while missing the regularity provided by score functions of our auxiliary models.

## Bayesian indirect inference [a response]

Posted in Books, Statistics, Travel, University life with tags ABC, auxiliary model, finite mixtures, indirect inference, score function, synthetic likelihood, University of Warwick on February 18, 2014 by xi'an*T**his Bayesian indirect inference paper by Chris Drovandi and Tony Pettitt was discussed on the ‘Og two weeks ago and Chris sent me the following comments.*

…unsurprisingly, the performances of ABC comparing true data of size n with synthetic data of size m>n are not great. However, there exists another way of reducing the variance in the synthetic data, namely by repeating simulations of samples of size n and averaging the indicators for proximity, resulting in a frequency rather than a 0-1 estimator. See e.g. Del Moral et al. (2009). In this sense, increasing the computing power reduces the variability of the ABC approximation. (And I thus fail to see the full relevance of Result 1.)

**T**aking the average of the indicators from multiple simulations will reduce the variability of the estimated ABC likelihood but because it is only still an unbiased estimate it will not alter the target and will not improve the ABC approximation (Andrieu and Roberts 2009). It will only have the effect of improving the mixing of MCMC ABC. Result 1 is used to contrast ABC II and BIL as they behave quite differently as n is increased.

The authors make several assumptions of unicity that I somewhat find unclear. While assuming that the MLE for the auxiliary model is unique could make sense (Assumption 2), I do not understand the corresponding indexing of this estimator (of the auxiliary parameter) on the generating (model) parameter θ. It should only depend on the generated/simulated data x. The notion of a noisy mapping is just confusing to me.

The dependence on θ is a little confusing I agree (especially in the context of ABC II methods). It starts to become more clear in the context of BIL. As n goes to infinity, the effect of the simulated data is removed and then we obtain the function φ(θ) (so we need to remember which θ simulated the data), which is referred to as the mapping or binding function in the II literature. If we somehow knew the binding function, BIL would proceed straightforwardly. But of course we don’t in practice, so we try to estimate it via simulated data (which, for computational reasons, needs to be a finite sample) from the true model based on theta. Thus we obtain a noisy estimate of the mapping. One way forward might be to fit some (non-parametric?) regression model to smooth out the noise and try to recover the true mapping (without ever taking n to infinity) and run a second BIL with this estimated mapping. I plan to investigate this in future work.

The assumption that the auxiliary score function at the auxiliary MLE for the observed data and for a simulated dataset (Assumption 3) is unique proceeds from the same spirit. I however fail to see why it matters so much. If the auxiliary MLE is the result of a numerical optimisation algorithm, the numerical algorithm may return local modes. This only adds to the approximative effect of the ABC-I schemes.

The optimiser failing to find the MLE (local mode) is certainly an issue shared by all BII methods, apart from ABC IS (which only requires 1 optimisation, so more effort to find the MLE can be applied here). Assuming the optimiser can obtain the MLE, I think the uniqueness assumptions makes sense. It basically says that, for a particular simulated dataset we would like a unique value for the ABC discrepancy function.

Given that the paper does not produce convergence results for those schemes, unless the auxiliary model contains the genuine model, such theoretical assumptions do not feel that necessary.

Actually, the ABC II methods will never converge to the true posterior (in general) due to lack of sufficiency. This is even the case if the true model is a special case of the auxiliary model! (in which case BIL can converge to the true posterior)

The paper uses normal mixtures as an auxiliary model: the multimodality of this model should not be such an hindrance (and reordering is transparent, i.e. does not “reduce the flexibility of the auxiliary model”, and does not “increase the difficulty of implementation”, as stated p.16).

The paper concludes from a numerical study to the superiority of the Bayesian indirect inference of Gallant and McCulloch (2009). Which simply replaces the true likelihood with the maximal auxiliary model likelihood estimated from a simulated dataset. (This is somehow similar to our use of the empirical likelihood in the PNAS paper.) It is however moderated by the cautionary provision that “the auxiliary model [should] describe the data well”. As for empirical likelihood, I would suggest resorting to this Bayesian indirect inference as a benchmark, providing a quick if possibly dirty reference against which to test more elaborate ABC schemes. Or other approximations, like empirical likelihood or Wood’s synthetic likelihood.

Unfortunately the methods are not quick (apart from ABC IS when the scores are analytic), but good approximations can be obtained. The majority of Bayesian methods that deal with intractable likelihoods do not target the true posterior (there are a couple of exceptions in special cases) and thus also suffer from some dirtiness, and BII does not escape from that. But, if a reasonable auxiliary model can be found, then I would suggest that (at least one of the) BII methods will be competitive.

On reflection for BIL it is not necessary for the auxiliary model to fit the data, since the generative model being proposed may be mis-specified and also not fit the data well. BIL needs an auxiliary model that mimics well the likelihood of the generative model for values of theta in non-negligible posterior regions. For ABC II, we are simply looking for a good summarisation of the data. Therefore it would seem useful if the auxiliary model did fit the data well. Note this process is independent of the generative model being proposed. Therefore the auxiliary model would be the same regardless of the chosen generative model. Very different considerations indeed.

Inspired by a discussion with Anthony Lee, it appears that the (Bayesian version) of synthetic likelihood you mentioned is actually also a BIL method but where the auxiliary model is applied to the summary statistic likelihood rather than the full data likelihood. The synthetic likelihood is nice from a numerical/computational point of view as the MLE of the auxiliary model is analytic.

## Bayesian indirect inference

Posted in Statistics, University life with tags ABC, auxiliary model, indirect inference, score function, University of Warwick on January 31, 2014 by xi'an**T**he paper with above title by Chris Drovandi and Tony Pettitt was presented by Chris Drovandi at a seminar in Warwick last week *(when I was not there yet)*. But the talk made me aware of the paper. It is mostly a review of existing works on ABC and indirect inference, which was already considered (as an alternative) in Fearnhead’s and Prangle’s Read Paper, with simulations to illustrate the differences. In particular, it seems to draw from the recent preprint by Gleim and Pigorsch (preprint that I need to read now!). Preprint that draws a classification of indirect inference versions of ABC.

**I**ndirect inference and its connections with ABC have been on my radar for quite a while, even though I never went farther than thinking of it, as it was developed by colleagues (and former teachers) at CREST, Christian Gouriéroux, Alain Monfort, and Eric Renault in the early 1990’s. Since it relies on an auxiliary model, somewhat arbitrary, indirect inference is rather delicate to incorporate within a Bayesian framework. In an ABC setting, indirect inference provides summary statistics (as estimators or scores) and possibly a distance. In their comparison, Drovandi and Pettitt analyse the impact of increasing the pseudo sample size in the simulated data. Rather unsurprisingly, the performances of ABC comparing true data of size n with synthetic data of size m>n are not great. However, there exists another way of reducing the variance in the synthetic data, namely by repeating simulations of samples of size n and averaging the indicators for proximity, resulting in a frequency rather than a 0-1 estimator. See e.g. Del Moral et al. (2009). In this sense, increasing the computing power reduces the variability of the ABC approximation. (And I thus fail to see the full relevance of Result 1.)

**T**he authors make several assumptions of unicity that I somewhat find unclear. While assuming that the MLE for the auxiliary model is unique could make sense (Assumption 2), I do not understand the corresponding indexing of this estimator (of the auxiliary parameter) on the generating (model) parameter θ. It should only depend on the generated/simulated data x. The notion of a noisy mapping is just confusing to me. The assumption that the auxiliary score function at the auxiliary MLE for the observed data and for a simulated dataset (Assumption 3) is unique proceeds from the same spirit. I however fail to see why it matters so much. If the auxiliary MLE is the result of a numerical optimisation algorithm, the numerical algorithm may return local modes. This only adds to the approximative effect of the ABC-I schemes. Given that the paper does not produce convergence results for those schemes, unless the auxiliary model contains the genuine model, such theoretical assumptions do not feel that necessary. The paper uses normal mixtures as an auxiliary model: the multimodality of this model should not be such an hindrance (and reordering is transparent, i.e. does not “reduce the flexibility of the auxiliary model”, and does not “increase the difficulty of implementation”, as stated p.16).

**T**he paper concludes from a numerical study to the superiority of the Bayesian indirect inference of Gallant and McCulloch (2009). Which simply replaces the true likelihood with the maximal auxiliary model likelihood estimated from a simulated dataset. (This is somehow similar to our use of the empirical likelihood in the PNAS paper.) It is however moderated by the cautionary provision that “the auxiliary model [should] describe the data well”. As for empirical likelihood, I would suggest resorting to this Bayesian indirect inference as a benchmark, providing a quick if possibly dirty reference against which to test more elaborate ABC schemes. Or other approximations, like empirical likelihood or Wood’s synthetic likelihood.