Scott Sisson’s ABC seminar in Paris [All about that Bayes]

On the “All about that Bayes” seminar tomorrow (Tuesday 21 at 3p.m., room 42, AgroParisTech, 16 rue Claude Bernard, Paris 5ième), Scott Sisson, School of Mathematics and Statistics at UNSW, and visiting Paris-Dauphine this month, will give a talk on

Approximate posteriors and data for Bayesian inference

Abstract
For various reasons, including large datasets and complex models, approximate inference is becoming increasingly common. In this talk I will provide three vignettes of recent work. These cover a) approximate Bayesian computation for Gaussian process density estimation, b) likelihood-free Gibbs sampling, and c) MCMC for approximate (rounded) data.

Australia’s burning and it ain’t gonna stop anytime soon…

freedom for women jailed in Iran

Three women are currently on hunger strike in Iranian jails to protest against their arbitrary detention under espionage charges and the dire conditions of these detentions. Two of them, Fariba Adelkhah and Nazanin Zaghari-Ratcliffe, are dual nationals with Iranian nationality, whose second nationality is not recognised by Iranian authorities and makes their release from jail the more unlikely. On the French side, Fariba Adelkhah, anthropologist, along with Roland Marchal, sociologist, is a researcher at Sciences Po Paris. They were arrested in June 2019. On the British side, Nazanin Zaghari-Ratcliffe (jailed in April 2016) is working with the Thomson Reuters Foundation, the Canadian news agency Thomson Reuters‘ charitable arm and Kylie Moore-Gilbert (jailed since October 2018) is a  Middle East Lecturer at the University of Melbourne‘s Asia Institute. Petitions have been launched to support them, but I wonder at the possibility to move the Iranian cynical stance other than through the respective governments of these women. (Obviously, there are many many other women and men jailed in Iran for political or other discriminatory reasons who should equally be freed.)

 

Australian theocracy

Examples from The Guardian at which discrimination based on religious arguments should become legal in Australia:

• A doctor may tell a transgender patient of their religious belief that God made men and women in his image and that gender is therefore binary (EM)

• A single mother who, when dropping her child off at daycare, may be told by a worker that she is sinful for denying her child a father (Public Interest Advocacy Centre)

• A woman may be told by a manager that women should submit to their husbands or that women should not be employed outside the home (PIAC)

• A student with disability may be told by a teacher their disability is a trial imposed by God (PIAC)

• A person of a minority faith may be told by a retail assistant from another religion that they are a “heathen destined for eternal damnation” (PIAC).

• A Catholic doctor refusing to provide contraception to all patients (EM) or to prescribe hormone treatment for gender transition (Equality Australia, Just Equal, LGBTI Health Alliance)

• A Catholic nurse who refused to participate in abortion procedures (EM) or to provide the morning-after pill to a woman admitted to hospital after a sexual assault (Equality Australia)

• A pharmacist refusing to provide the pill to women for contraceptive use (EM), or hormone treatment (Public Interest Advocacy Centre, LGBTI Health Alliance)

• A doctor could refuse to prescribe post-exposure prophylaxis (PEP) within the required 72-hour window to a patient whose condom broke during a sexual encounter on the basis of religious beliefs that forbid sexual activity outside of marriage (Equality Australia)

• A psychiatrist could say to a woman with depression that “she should be looking forward to the kingdom of heaven”. (Equality Australia)

• A Jewish school may require that its staff and students be Jewish and accordingly refuse to hire or admit someone because they were not Jewish (EM)

• A student attends the same religious school through their primary and secondary education. At 16 they lose faith in the religion of the school and tell a teacher that they are now agnostic. The school would be able to expel, suspend or otherwise punish, for example, give detention to the student (PIAC)

• A homeowner seeking a tenant for their spare room may require that the tenant be of the same religious belief or activity as the homeowner (EM).

Australia’s burning…

a statistic with consequences

In the latest Significance, there was a flyer with some members updates, an important one being that Sylvia Richardson had been elected the next president of the Royal Statistical Society. Congratulations to my friend Sylvia! Another item was that the publication of the 2018 RSS Statistic of the Year has led an Australian water company to switch from plastic to aluminum. Hmm, what about switching to nothing and supporting a use-your-own bottle approach? While it is correct that aluminum cans can be 100% made of recycled aluminum, this water company does not seem to appear to make any concerted effort to ensure its can are made of recycled aluminum or to increase the recycling rate for aluminum in Australia towards achieving those of Brazil (92%) or Japan (86%). (Another shocking statistic that could have been added to the 90.5% non-recycled plastic waste [in the World?] is that a water bottle consumes the equivalent of one-fourth of its contents in oil to produce.) Another US water company still promotes water bottles as one of the most effective and inert carbon capture & sequestration methods”..! There is no boundary for green-washing.

truncated Normal moments

An interesting if presumably hopeless question spotted on X validated: a lower-truncated Normal distribution is parameterised by its location, scale, and truncation values, μ, σ, and α. There exist formulas to derive the mean and variance of the resulting distribution,  that is, when α=0,

and

but there is no easy way to choose (μ, σ) from these two quantities. Beyond numerical resolution of both equations. One of the issues is that ( μ, σ) is not a location-scale parameter for the truncated Normal distribution when α is fixed.