Research lectures, seminars and events
The events listed in this area are research seminars, workshops and lectures hosted by Durham University departments and research institutes. If you are not a member of the University, but wish to enquire about attending one of the events please contact the organiser or host department.
|May 2018||July 2018|
Events for 20 June 2018
Nawapon Nakharutai: Odds and free coupon: modelling by desirability axioms and checking avoiding sure loss via the Choquet integral
In the UK betting market, bookmakers often offer a free coupon to new customers. These free coupons allow the customer to place extra bets, at lower risk, in combination with the usual betting odds. We are interested in whether a customer can exploit these free coupons in order to make a sure gain, and if so, how the customer can achieve this. To answer this question, we model the odds and free coupon as a set of desirable gambles for the bookmaker.
We show that we can use the Choquet integral to check whether this set of desirable gambles incurs sure loss for the bookmaker, and hence, results in a sure gain for the customer. In the latter case, we also show how a customer can determine the combination of bets that make the best possible gain, based on complementary slackness.
As an illustration, we look at some actual betting odds in the market and find that, without free coupons, the set of desirable gambles derived from those odds avoids sure loss. However, with free coupons, we identify some combinations of bets that customers could place in order to make a guaranteed gain.
Evidence suggests that changes in the urine output and blood chemistries indicate injury to the kidney or impairment of kidney function. These changes are warnings of serious clinical consequences, but traditionally most studies emphasise the most severe reduction in kidney function. It has only been recently that minor decreases of kidney function have been recognised as potentially important in the critically ill. Identifying and intervening in patients with minor decreases in kidney function is clinically important as this can prevent patients from reaching more severe reductions in kidney failure.
The KDIGO (Kidney Disease Improving Global Outcomes) guidelines are a clinical practice guideline for the diagnosis, evaluation, prevention, and treatment of kidney disease and are currently used worldwide to identify a whole range of levels of kidney failure. In this presentation I will discuss how the KDIGO guidelines are too sensitive when classifying adverse outcomes due to kidney deterioration and show how dynamic models and Bayesian forecasting offer a powerful framework for the modelling and analysis of noisy time series which are subject to abrupt changes in pattern.