Archive for weather forecasters
Fusing Simulation with Data Science [ CRiSM workshop, 18-9/07/23]
Posted in Statistics with tags climate change, Coventry, CRiSM, data assimilation, fusion, Met Office, model emulation, probabilistic numerics, University of Warwick, weather forecasters, workshop on April 7, 2023 by xi'an
amber warning
Posted in Statistics with tags Austin, England, running, snow, University of Warwick, Warwickshire, weather forecasters on December 10, 2017 by xi'anJust saw this severe Met warning of snow over Warwickshire and neighbouring counties… The campus is indeed covered with snow, but not that heavily. Yet. (It is comparatively mild in Austin, Texas, even though the icy wind turned my fingers to iciles during my morning run there!)
Amber warning of snow
- From: 0810 on Sun 10 December
- To: 1800 on Sun 10 December
Updated 6 hours ago Active
A spell of heavy snow is likely over parts of Wales, the Midlands and parts of Northern and Eastern England on Sunday.
Road, rail and air travel delays are likely, as well as stranding of vehicles and public transport cancellations. There is a good chance that some rural communities could become cut off.This is an update to extend the warning area as far south as Gloucestershire, Wiltshire, Oxfordshire, Buckinghamshire, Hertfordshire and Essex.
Terry Tao on Bayes… and Trump
Posted in Books, Kids, Statistics, University life with tags grading, multiple answer test, proper scoring rule, scoring rule, Terry Tao, uncertainty, weather forecasters, what's new on June 13, 2016 by xi'an“From the perspective of Bayesian probability, the grade given to a student can then be viewed as a measurement (in logarithmic scale) of how much the posterior probability that the student’s model was correct has improved over the prior probability.” T. Tao, what’s new, June 1
Jean-Michel Marin pointed out to me the recent post of Terry Tao on setting a subjective prior for allocating partial credits to multiple answer questions. (Although I would argue that the main purpose of multiple answer questions is to expedite grading!) The post considers only true-false questionnaires and the case when the student produces a probabilistic assessment of her confidence in the answer. In the format of a probability p for each question. The goal is then to devise a grading principle, f, such that f(p) goes to the right answer and f(1-p) to the wrong answer. This sounds very much like scoring weather forecasters and hence designing proper scoring rules. Which reminds me of the first time I heard a talk about this: it was in Purdue, circa 1988, and Morrie DeGroot gave a talk on scoring forecasters, based on a joint paper he had written with Susie Bayarri. The scoring rule is proper if the expected reward leads to pick p=q when p is the answer given by the student and q her true belief. Terry Tao reaches the well-known conclusion that the grading function f should be f(p)=log²(2p) where log² denotes the base 2 logarithm. One property I was unaware of is that the total expected score writes as N+log²(L) where L is the likelihood associated with the student’s subjective model. (This is the only true Bayesian aspect of the problem.)
An interesting and more Bayesian last question from Terry Tao is about what to do when the probabilities themselves are uncertain. More Bayesian because this is where I would introduce a prior model on this uncertainty, in a hierarchical fashion, in order to estimate the true probabilities. (A non-informative prior makes its way into the comments.) Of course, all this leads to a lot of work given the first incentive of asking multiple choice questions…
One may wonder at the link with scary Donald and there is none! But the next post by Terry Tao is entitled “It ought to be common knowledge that Donald Trump is not fit for the presidency of the United States of America”. And unsurprisingly, as an opinion post, it attracted a large number of non-mathematical comments.
