Archive for ABC in Rome

ABC in Sydney, July 3-4, 2014!!!

Posted in pictures, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , on February 12, 2014 by xi'an

Sydney Opera from Sydney Harbour Bridge, Sydney, July 14, 2012After ABC in Paris in 2009, ABC in London in 2011, and ABC in Roma last year, things are accelerating since there will be—as I just learned—  an ABC in Sydney next July (not June as I originally typed, thanks Robin!). The workshop on the current developments of ABC methodology thus leaves Europe to go down-under and to take advantage of the IMS Meeting in Sydney on July 7-10, 2014. Hopefully, “ABC in…” will continue its tour of European capitals in 2015! To keep up with an unbroken sequence of free workshops, Scott Sisson has managed to find support so that attendance is free of charge (free as in “no registration fee at all”!) but you do need to register as space is limited. While I would love to visit UNSW and Sydney once again and attend the workshop, I will not, getting ready for Cancún and our ABC short course there.

fie on fee frenzy!

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , , , , , , , on June 11, 2013 by xi'an

London by Delta, Dec. 14, 2011In the past years, I noticed a clear inflation on conference fees, inflation that I feel unjustified… I already mentioned the huge $720 fees for the Winter Simulation Conference (WSC 2012), which were certainly not all due to the heating bill! Even conferences held by and in universities or societies seem to face the same doom: to stick to conferences I will attend—and do support, to the point of being directly or indirectly involved—, take for instance Bayes 250 in London (RSS Headquarters), £135, Bayes 250 at Duke, $190, both one day-long, and O-Bayes 2013, also at Duke, $480 (in par with JSM fees)… While those later conferences include side “benefits” like meals and banquet, the amount remains large absolutive. Too large. And prohibitive for participants from less-favoured countries (possibly including speakers themselves in the case of O-Bayes 2013). And also counter-productive in the case of both Bayes 250 conferences since we want to get together to celebrate two and a half centuries of Bayesian statistics. Since most of the talks there will be partly commemorative, rather than on the brink of research, I fear some people may have to make a choice to allocate their meagre research funds to other conferences. And I do not understand why universities now consider organising meetings as a source of income rather than as a natural part of their goals.

Now, you may ask, and what about MCMski on which I have more than a modicum of control..?! Well, the sole cost there is renting the conference centre in Chamonix, which is the only place I knew where a large conference could be held. Apart from that, no frill! The coffee breaks will be few and frugal, there will be no free lunch or breakfast or banquet, and no one will get a free entry or a paid invitation. As a result, the registration fee is only 170€ for three days (plus a free satellite meeting the next day), an amount computed on an expected number of participants of 150 and which could lead me to pay the deficit from my own research grants in case I am wrong.  (And may I recall the “ABC in…” series, which has been free of fees so far!)

My point, overall, is that we should aim at more frugal meetings, in order to attract larger and more diverse crowds (even though fees are only part of the equation, lodging and travelling can be managed to some extent as long as the workshop is not in too an exotic location).

accurate ABC: comments by Oliver Ratman [guest post]

Posted in R, Statistics, University life with tags , , , , , , , , on May 31, 2013 by xi'an

Here are comments by Olli following my post:

I think we found a general means to obtain accurate ABC in the sense of matching the posterior mean or MAP exactly, and then minimising the KL distance between the true posterior and its ABC approximation subject to this condition. The construction works on an auxiliary probability space, much like indirect inference. Now, we construct this probability space empirically, this is where our approach differs first from indirect inference and this is where we need the “summary values” (>1 data points on a summary level; see Figure 1 for clarification). Without replication, we cannot model the distribution of summary values but doing so is essential to construct this space. Now, lets focus on the auxiliary space. We can fiddle with the tolerances (on a population level) and m so that on this space, the ABC approximation has the aforesaid properties. All the heavy technical work is in this part. Intuitively, as m increases, the power increases for sufficiently regular tests (see Figure 2) and consequently, for calibrated tolerances, the ABC approximation on the auxiliary space goes tighter. This offsets the broadening effect of the tolerances, so having non-identical lower and upper tolerances is fine and does not hurt the approximation. Now, we need to transport the close-to-exact ABC approximation on the auxiliary space back to the original space. We need some assumptions here, and given our time series example, it seems these are not unreasonable. We can reconstruct the link between the auxiliary space and the original parameter space as we accept/reject. This helps us understand (with the videos!) the behaviour of the transformation and to judge if its properties satisfy the assumptions of Theorems 2-4. While we offer some tools to understand the behaviour of the link function, yes, we think more work could be done here to improve on our first attempt to accurate ABC.

Now some more specific comments:
“The paper also insists over and over on sufficiency, which I fear is a lost cause.” To clarify, all we say is that on the simple auxiliary space, sufficient summaries are easily found. For example, if the summary values are normally distributed, the sample mean and the sample variance are sufficient statistics. Of course, this is not the original parameter space and we only transform the sufficiency problem into a change of variable problem. This is why we think that inspecting and understanding the link function is important.

“Another worry is that the … test(s) rel(y) on an elaborate calibration”. We provide some code here  for everyone to try out. In our examples, this did not slow down ABC considerably. We generally suppose that the distribution of the summary values is simple, like Gaussian, Exponential, Gamma, ChiSquare, Lognormal. In these cases, the ABC approximation takes on an easy-enough-to-calibrate-fast functional form on the auxiliary space.

“This Theorem 3 sounds fantastic but makes me uneasy: unbiasedness is  a sparse property that is rarely found in statistical problems. … Witness the use of “essentially unbiased” in Fig. 4.” What Theorem 3 says is that if unbiasedness can be achieved on the simple auxiliary space, then there are regularity conditions under which these properties can be transported back to the original parameter space. We hope to illustrate these conditions with our examples, and to show that they hold in quite general cases such as the time series application. The thing in Figure 4 is that the sample autocorrelation is not an unbiased estimator of the population autocorrelation. So unbiasedness does not quite hold on the auxiliary space and the conditions of Theorem 3 are not satisfied. Nevertheless, we found this bias to be rather negligible in our example and the bigger concern was the effect of the link function.

And here are Olli’s slides:

ABC in Roma, May 30-31, 2013!!!

Posted in pictures, Statistics, Travel, University life, Wines with tags , , , , , , on July 24, 2012 by xi'an

After ABC in Paris in 2009 and ABC in London in 2013, the ABC workshop on the current developments of ABC methodology continues its tour of European capitals! It will take place next year in Rome over two days to allow for travel from Paris and London (no EuRomaStar yet!). ABC in Rome is organised by Brunero Liseo and his colleagues at Roma 1 and Roma 3 Università, and sponsored by La Sapienza Università di Roma. Following the previous meetings, in Paris and London, the field still sees rapid methodology progress and an increased number of applications in a wider range of scientific areas, as no doubt readers of the ‘Og are aware! ABC in Rome (ABCiR) will hopefully bring together leading researchers in the field, with focus on

  •  applications of ABC to real world problems
  • recent computational advances in ABC
  • comparative efficiency of ABC methods with respect to alternative methodologies
  • model selection and model checking in the ABC framework.

As in the previous meetings, attendance is free of charge (free as in “no registration fee at all”!) but you do need to register as space is strictly limited. (For ABC in London, the waiting list was more than 100 persons long…) If you wish to present a poster then please email the organisation committee with a brief abstract. We particularly encourage posters from young participants and posters detailing recent software implementations of ABC methods and computational advance.

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