Archive for Trondheim


Posted in pictures, Travel with tags , , , , , , on January 12, 2018 by xi'an

a new paradigm for improper priors

Posted in Books, pictures, Statistics, Travel with tags , , , , , , , , on November 6, 2017 by xi'an

Gunnar Taraldsen and co-authors have arXived a short note on using improper priors from a new perspective. Generalising an earlier 2016 paper in JSPI on the same topic. Which both relate to a concept introduced by Rényi (who himself attributes the idea to Kolmogorov). Namely that random variables measures are to be associated with arbitrary measures [not necessarily σ-finite measures, the later defining σ-finite random variables], rather than those with total mass one. Which allows for an alternate notion of conditional probability in the case of σ-finite random variables, with the perk that this conditional probability distribution is itself of mass 1 (a.e.).  Which we know happens when moving from prior to proper posterior.

I remain puzzled by the 2016 paper though as I do not follow the meaning of a random variable associated with an infinite mass probability measure. If the point is limited to construct posterior probability distributions associated with improper priors, there is little value in doing so. The argument in the 2016 paper is however that one can then define a conditional distribution in marginalisation paradoxes à la Stone, Dawid and Zidek (1973) where the marginal does not exist. Solving with this formalism the said marginalisation paradoxes as conditional distributions are only defined for σ-finite random variables. Which gives a fairly different conclusion that either Stone, Dawid and Zidek (1973) [with whom I agree, namely that there is no paradox because there is no “joint” distribution] or Jaynes (1973) [with whom I less agree!, in that the use of an invariant measure to make the discrepancy go away is not a particularly strong argument in favour of this measure]. The 2016 paper also draws an interesting connection with the study by Jim Hobert and George Casella (in Jim’s thesis) of [null recurrent or transient] Gibbs samplers with no joint [proper] distribution. Which in some situations can produce proper subchains, a phenomenon later exhibited by Alan Gelfand and Sujit Sahu (and Xiao-Li Meng as well if I correctly remember!). But I see no advantage in following this formalism, as it does not impact whether the chain is transient or null recurrent, or anything connected with its implementation. Plus a link to the approximation of improper priors by sequences of proper ones by Bioche and Druihlet I discussed a while ago.

fiducial inference

Posted in Books, Mountains, pictures, Running, Statistics, Travel with tags , , , , , , , , , , on October 30, 2017 by xi'an

In connection with my recent tale of the many ε’s, I received from Gunnar Taraldsen [from Tronheim, Norge] a paper [jointly written with Bo Lindqvist and just appeared on-line in JSPI] on conditional fiducial models.

“The role of the prior and the statistical model in Bayesian analysis is replaced by the use of the fiducial model x=R(θ,ε) in fiducial inference. The fiducial is obtained in this case without a prior distribution for the parameter.”

Reading this paper after addressing the X validated question made me understood better the fundamental wrongness of fiducial analysis! If I may herein object to Fisher himself… Indeed, when writing x=R(θ,ε), as the representation of the [observed] random variable x as a deterministic transform of a parameter θ and of an [unobserved] random factor ε, the two random variables x and ε are based on the same random preimage ω, i.e., x=x(ω) and ε=ε(ω). Observing x hence sets a massive constraint on the preimage ω and on the conditional distribution of ε=ε(ω). When the fiducial inference incorporates another level of randomness via an independent random variable ε’ and inverts x=R(θ,ε’) into θ=θ(x,ε’), assuming there is only one solution to the inversion, it modifies the nature of the underlying σ-algebra into something that is incompatible with the original model. Because of this sudden duplication of the random variates. While the inversion of this equation x=R(θ,ε’) gives an idea of the possible values of θ when ε varies according to its [prior] distribution, it does not account for the connection between x and ε. And does not turn the original parameter into a random variable with an implicit prior distribution.

As to conditional fiducial distributions, they are defined by inversion of x=R(θ,ε), under a certain constraint on θ, like C(θ)=0, which immediately raises a Pavlovian reaction in me, namely that since the curve C(θ)=0 has measure zero under the original fiducial distribution, how can this conditional solution be uniquely or at all defined. Or to avoid the Borel paradox mentioned in the paper. If I get the meaning of the authors in this section, the resulting fiducial distribution will actually depend on the choice of σ-algebra governing the projection.

“A further advantage of the fiducial approach in the case of a simple fiducial model is that independent samples are produced directly from independent sampling from [the fiducial distribution]. Bayesian simulations most often come as dependent samples from a Markov chain.”

This side argument in “favour” of the fiducial approach is most curious as it brings into the picture computational aspects that do not have any reason to be there. (The core of the paper is concerned with the unicity of the fiducial distribution in some univariate settings. Not with computational issues.)

art brut

Posted in pictures, Travel with tags , , , , on July 8, 2012 by xi'an


Posted in Mountains, pictures, Travel with tags , , , , , , on June 10, 2012 by xi'an

I did not see any troll in this land of the giants. Presumably, we were flying too high…

semi-automatic ABC [reply]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on June 5, 2012 by xi'an

When I came back from LGM2012 in Trondheim, I found the latest issue of Series B on my desk. It is much thicker than in “my” days, with about 250 pages in this June 2012 issue! (One reason is that it contains two Read Papers with their discussions, amounting to 110 pages of the journal.) The first Read Paper was “Catching up faster by switching sooner” by van Erven, Grünwald and de Rooij, that we discussed with Nicolas Chopin. There are also comments (among others!) from Stephen Lauritzen, Iain Murray, and Aki Vehtari, who also spoke about Bayesian model evaluation tools at LGM2012. The second Read Paper is Fearnhead’s and Prangle’s semi-automatic ABC that I discussed last December. I have already posted about this Read Paper and used some of the discussion in preparing my ABC PhD class in Roma.  However, the remark we made in our discussion with Jean-Michel Marin that the Bayes factor would not be a pertinent summary statistic for model choice is wrong, as shown by Dennis Prangle in his poster at the workshop in Bristol. And, when reading the reply by Paul Fearnhead and Dennis Prangle, I do not see a satisfactory answer to my demand of more formal conditions for Theorem 2 and its corollary, the convergence of the noisy ABC posterior to the true parameter (page 425), to apply. (Such results exist in indirect inference.)

Trondheim, wooden houses

Posted in pictures, Travel with tags , , on June 3, 2012 by xi'an

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