Latest Posts
10th April, 2016
A Jump Back to Population Estimation
Model uncertainty can be handled with using an MCMC algorithm that jumps between models.
1st April, 2016
No Time to Read My Blog? Try Spritzing
Can Machine Learning and Classification be applied in a way to help people read quicker?
23rd March, 2016
A Lesson in Simplicity from Dijkstra
Solving the Shortest Path Problem using integer programming can be quite inefficient. Thankfully,
Dijkstra's algorithm uses the information in a much better way.
18th March, 2016
Networking is Everything (Nearly)
Networks are everywhere. Prof. Mirchandani actually claimed to solve most real world problems, one needs knowledge of
probability and of networks.
12th March, 2016
Descrete Hopsital Queues
For a hospital ward, the instantaneous bed demand is less important than the daily planning of admissions on
a daily basis.
6th March, 2016
Infinitely Many Doctors? Hmmm...
I have chosen to look at is Infinite Server Queuing Theory and looking at its applications within Health Care Services.
3rd March, 2016
Estimating Near Invisible Populations
Prof. Ruth King gave a masterclass based on how to estimate a population when counting
is infeasible.
26th February, 2016
Problems with Police Cars and Pricing
Probelm Solving Days are opportunities for the STOR-i students
to spend a day on a problem faced by industry.
21st February, 2016
Changing Extremes
Stationary time series are rare. Seasonality and trend are the rule rather
than the exception.
16th February, 2016
Just Do it a Lot and Take the Average!
We did not cover SAA in detail in the Master Class, but the poster has made us look at it in more detail.
8th February, 2016
Tails, Droughts and Extremes
A little bit about Extreme Value Theory and how it can be applied to modelling groundwater levels.
5th February, 2016
Gaussian Processes
I would like to discuss a section of the Statistical Learning talk, Gaussian Processes, given by Chris Nemeth.
31st January, 2016
Can Hamiltonian Win At Monte Carlo?
Chris Sherlock gave us the basic idea of how Hamiltonian Monte Carlo simulates from a posterior distribution.
26th January, 2016
Optimising with Ants and Ice-cream Cones
As part of our research topics, we have a brief look into Stochastic Search, Multi-Objective and
Conic Optimisation.
22th January, 2016
Optimisation and Error in Simul?ation
This week, STOR-i put on a Master Class in Stochastic Simulation Design and Analytics from Prof.
Barry Nelson.
17th January, 2016
Can MCMC Be Updated For The Age Of Big Data?
Are tools such as MCMC, a particular favourite, up to the new challenges of dealing with such large masses of data?
15th January, 2016
Why Not Let Bandits in to Help in Clinical Trials?
Could Multi-Armed Bandit Problems be used to help when implementing clinical trials?
10th January, 2016
STOR-i Conference 2016
In the last few days I have attended the two day annual
STOR-i Conference...