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MRes Posts

Blog 1

10th April, 2016

A Jump Back to Population Estimation

Model uncertainty can be handled with using an MCMC algorithm that jumps between models.

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Blog 1

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?

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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.

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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.

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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.

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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.

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3rd March, 2016

Estimating Near Invisible Populations

Prof. Ruth King gave a masterclass based on how to estimate a population when counting is infeasible.

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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.

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21st February, 2016

Changing Extremes

Stationary time series are rare. Seasonality and trend are the rule rather than the exception.

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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.

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8th February, 2016

Tails, Droughts and Extremes

A little bit about Extreme Value Theory and how it can be applied to modelling groundwater levels.

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5th February, 2016

Gaussian Processes

I would like to discuss a section of the Statistical Learning talk, Gaussian Processes, given by Chris Nemeth.

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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.

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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.

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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.

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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?

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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?

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Blog 1

10th January, 2016

STOR-i Conference 2016

In the last few days I have attended the two day annual STOR-i Conference...

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