Welcome to the Stats4Grads website! Here you will find all the information about the seminar series.
Stats4Grads is a weekly seminar in statistics, organised by and for postgraduate students. We meet on Wednesdays from 1pm-2pm, usually in CM105, with tea, coffee and biscuits provided by the Department of Mathematical Sciences.
Stats4Grads is a great chance to see the other ways postgraduate students use statistics: statisticians can better understand the importance and applications of statistics, how the subject is being used in current "real-world" problems, and what techniques and approaches are needed in the future. Meanwhile, the non-statisticians can learn about new methods and techniques being developed, and get help and insight from students who may have a deeper understanding of the theory behind statistical methods.
Feel free to invite a friend or collaborator from another institution or department to give a talk if they're in town!
Organiser: Clare Wallace. For information or to give a talk contact: firstname.lastname@example.org .
Stats4Grads Timetable 2018/2019
The following dates are free this term: 12 December.
Cost effective component swapping to increase system reliability
Speaker: Aesha Najem, Department of Mathematical Sciences, Durham University
Wednesday 21st November 2018: 1pm, CM105
One of the strategies that might be considered to enhance reliability and resilience of a system is swapping components when a component fails, so replacing it by another component from the system which is still functioning. This presentation considers cost effective component swapping to increase system reliability. The cost is discussed in two scenarios, namely fixed cost and time dependent cost for system failure.
Random set theory for frequentist inferences
Speaker: Daniel Krpelik, Department of Mathematical Sciences, Durham University
Wednesday 14th November 2018: 1pm, CM105
Recently, several inferential methods based on the random sets theory were proposed in the literature. Among those, we would like to focus on Confidence Structures. These can be seen as a generalisation of the inferential approach based on Confidence Distributions. In those, the result of the inference is a probability distribution over the range of parameter of interest which can be used to construct confidence intervals and test hypotheses on any level of significance. Using the random set models allows us to seamlessly derive approaches for analysing censored observations without any assumptions about the underlying censoring model whilst retaining the coverage properties of the confidence distributions. We will show the basic ideas behind the concept of confidence structures and demonstrate its use on reliability analysis of a simple system based on a set of censored observations of lifetimes of its components.
History Matching techniques applied to petroleum reservoir: discussing MCMC as sampling technique
Speaker: Helena Nandi Formentin, Department of Mathematical Sciences, Durham University
Wednesday 7th November 2018: 1pm, CM107
In petroleum engineering, reservoir simulation models are representations of real petroleum fields used in production forecast and decision-making process. Observed dynamic data (e.g. bottom-hole pressure and oil production) support the calibration of reservoir models. We use History Matching techniques to reduce our highly-dimensional input space - which contains parameters such as porosity and permeability and fluid properties - through the assimilation of measured data. We use emulation techniques to explore a simplified simulator of a reservoir model, and HM processes to reduce the simulator’s input space. In this section, we will discuss MCMC techniques applied to sample in a reduced and complex space.
Maintenance Record Labelling of Wind Turbine Data for Fault Prognosis
Wednesday 24th October 2018:
Speaker: Roger Cox, Department of Engineering, Durham University
Wednesday 31st October, 2018: 1pm, CM105
A set of methods are being developed for the determination of the health history of mechanical plant. These are to be applied both in offshore wind turbine maintenance trouble shooting (fault diagnosis) and in condition based maintenance (fault prognosis). One of the methods used is Bernoulli Naive Bayes classification.
Introduction to Stats4Grads
Come along to CM105 on Wednesday 24th October at 1pm to get to know your fellow statisticians! We'll introduce ourselves and our research area (briefly!), and then just have an informal chat.
Of course, the introductory meeting wouldn't be complete without free pizza ;)
For details of previous years' seminars, click here
Back to the Statistics Seminar list.