Using models for planning in response to a serious situation, such as during a pandemic, requires thorough quantification of the uncertainty of the predictions made by those models Martin A Tanner Noelle I. Samia Northwestern @ctds_unisyd UTEP Roman Marchant

Why does a Chinese lab keep getting accused of being the coronavirus source, despite zero evidence? abc.net.au/news/2020-04-1…

(Some) forecasting for COVID-19 has failed: a discussion of Taleb and Ioannidis et al. statmodeling.stat.columbia.edu/2020/06/17/som…

two year postdoc developing methods for intractable likelihood models in cognitive science. See external-careers.jobs.unsw.edu.au/ci/en/job/5029…

After a long journey, our paper "A synthetic likelihood approach for intractable Markov random fields" finally finds its home at #ComputationalStatistics rdcu.be/cRkkK #MarkovRandomField #PottsModel

HTI's Wanchuang Zhu & Sally Cripps worked on a new study combining cutting-edge Bayesian network modelling and informed analysis to unravel complex web causal factors – a methodology with potential to tackle other complex social and policy challenges. uts.edu.au/human-technolo…

bondi beach now!

rock!