This paper by Andrew Gelman and Christian Hennig calls for the abandonment of the terms objective and subjective in (not solely Bayesian) statistics. And argue that there is more than mere prior information and data to the construction of a statistical analysis. The paper is articulated as the authors’ proposal, followed by four application examples, then a survey of the philosophy of science perspectives on objectivity and subjectivity in statistics and other sciences, next to a study of the subjective and objective aspects of the mainstream statistical streams, concluding with a discussion on the implementation of the proposed move. Continue reading
Archive for the Statistics Category
In conjunction with our reliable ABC model choice via random forest paper, about to be resubmitted to Bioinformatics, we have contributed an R package called abcrf that produces a most likely model and its posterior probability out of an ABC reference table. In conjunction with the realisation that we could devise an approximation to the (ABC) posterior probability using a secondary random forest. “We” meaning Jean-Michel Marin and Pierre Pudlo, as I only acted as a beta tester!
The package abcrf consists of three functions:
- abcrf, which constructs a random forest from a reference table and returns an object of class `abc-rf’;
- plot.abcrf, which gives both variable importance plot of a model choice abc-rf object and the projection of the reference table on the LDA axes;
- predict.abcrf, which predict the model for new data and evaluate the posterior probability of the MAP.
An illustration from the manual:
data(snp) data(snp.obs) mc.rf <- abcrf(snp[1:1e3, 1], snp[1:1e3, -1]) predict(mc.rf, snp[1:1e3, -1], snp.obs)
Along with David Frazier and Gael Martin from Monash University, Melbourne, we have just completed (and arXived) a paper on the (Bayesian) consistency of ABC methods, producing sufficient conditions on the summary statistics to ensure consistency of the ABC posterior. Consistency in the sense of the prior concentrating at the true value of the parameter when the sample size and the inverse tolerance (intolerance?!) go to infinity. The conditions are essentially that the summary statistics concentrates around its mean and that this mean identifies the parameter. They are thus weaker conditions than those found earlier consistency results where the authors considered convergence to the genuine posterior distribution (given the summary), as for instance in Biau et al. (2014) or Li and Fearnhead (2015). We do not require here a specific rate of decrease to zero for the tolerance ε. But still they do not hold all the time, as shown for the MA(2) example and its first two autocorrelation summaries, example we started using in the Marin et al. (2011) survey. We further propose a consistency assessment based on the main consistency theorem, namely that the ABC-based estimates of the marginal posterior densities for the parameters should vary little when adding extra components to the summary statistic, densities estimated from simulated data. And that the mean of the resulting summary statistic is indeed one-to-one. This may sound somewhat similar to the stepwise search algorithm of Joyce and Marjoram (2008), but those authors aim at obtaining a vector of summary statistics that is as informative as possible. We also examine the consistency conditions when using an auxiliary model as in indirect inference. For instance, when using an AR(2) auxiliary model for estimating an MA(2) model. And ODEs.
Like last year, NIPS will be hosted in Montréal, Québec, Canada, and like last year there will be an ACB NIPS workshop. With a wide variety of speakers and poster presenters. There will also be a probabilistic integration NIPS workshop, to which I have been invited to give a talk, following my blog on the topic! Workshops are on December 11 and 12, and I hope those two won’t overlap so that I can enjoy both at length (before flying back to London for CFE 2015…)
Update: they do overlap, both being on December 11…
At the last (European) AISTATS 2014, I agreed to be the program co-chair for AISTATS 2016, along with Arthur Gretton from the Gatsby Unit, at UCL. (AISTATS stands for Artificial Intelligence and Statistics.) Thanks to Arthur’s efforts and dedication, as the organisation of an AISTATS meeting is far more complex than any conference I have organised so far!, the meeting is taking shape. First, it will take place in Cadiz, Andalucía, Spain, on May 9-11, 2016. (A place more related to the conference palm tree logo than the previous location in Reykjavik, even though I would be the last one to complain it took place in Iceland!)
Second, the call for submissions is now open. The process is similar to other machine learning conferences in that papers are first submitted for the conference proceedings, then undergo a severe and tight reviewing process, with a response period for the authors to respond to the reviewers’ comments, and that only the accepted papers can be presented as posters, some of which are selected for an additional oral presentation. The major dates for submitting to AISTATS 2016 are
|Proceedings track paper submission deadline||23:59 UTC Oct 9, 2015|
|Proceedings track initial reviews available||Nov 16, 2015|
|Proceedings track author feedback deadline||Nov 23, 2015|
|Proceedings track paper decision notifications||Dec 20, 2015|
I was quite impressed by the quality and intensity of the AISTATS 2014 conference, which is why I accepted so readily being program co-chair, and hence predict an equally rewarding AISTATS 2016, thus encouraging all interested ‘Og’s readers to consider submitting a paper there! Even though I confess it will make a rather busy first semester for 2016, between MCMSki V in January, the CIRM Statistics month in February, the CRiSM workshop on Eatimating constants in April, AISTATS 2016 thus in May, and ISBA 2016 in June…