Category: logistic regression

likelihood-free inference by ratio estimation

“This approach for posterior estimation with generative models mirrors the approach of Gutmann and Hyvärinen (2012) for the estimation of unnormalised models. The main difference is that here we classify between two simulated data sets while Gutmann and Hyvärinen (2012) classified between the observed data and simulated reference data.” A 2018 arXiv posting by Owen […]

conditional noise contrastive estimation

At ICML last year, Ciwan Ceylan and Michael Gutmann presented a new version of noise constrative estimation to deal with intractable constants. While noise contrastive estimation relies upon a second independent sample to contrast with the observed sample, this approach uses instead a perturbed or noisy version of the original sample, for instance a Normal […]

sampling and imbalanced

Deborshee Sen, Matthias Sachs, Jianfeng Lu and David Dunson have recently arXived a sub-sampling paper for  classification (logistic) models where some covariates or some responses are imbalanced. With a PDMP, namely zig-zag, used towards preserving the correct invariant distribution (as already mentioned in an earlier post on the zig-zag zampler and in a recent Annals […]

assessing MCMC convergence

When MCMC became mainstream in the 1990’s, there was a flurry of proposals to check, assess, and even guarantee convergence to the stationary distribution, as discussed in our MCMC book. Along with Chantal Guihenneuc and Kerrie Mengersen, we also maintained for a while a reviewww webpage categorising theses. Niloy Biswas and Pierre Jacob have recently […]

how a hiring quota failed [or not]

This week, Nature has a “career news” section dedicated to how hiring quotas [may have] failed for French university hiring. And based solely on a technical report by a Science Po’ Paris researcher. The hiring quota means that every hiring committee for a French public university hiring committee must be made of at least 40% […]