Ensemble | Dealing with input modelling error in simulation and beyond…

Ensemble | Dealing with input modelling error in simulation and beyond…

Topic: Input modelling error in simulation

Guest Speaker: Lucy Morgan, Lancaster University

Date: Tuesday 25 February 2020

Time: 12:00 – 13:00 GMT

Location: Peak HQ, 12th Floor, Neo Building, Charlotte Street. Manchester, M1 4ET.


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Event Description

Ensemble is an artificial intelligence, machine learning and data science event held regularly at Peak in Manchester. Like an ensemble model, the event will allow learning through exposure to a diverse set of experiences. Each month, a top researcher from the fields of computer science, statistics or operational research will be invited to present their work. The audience will be made up of data scientists, software engineers, researchers and company CIOs from tech companies and universities.

This will be a great opportunity to learn and network with fellow professionals and academics in the field.

Talk abstract:

Simulation models aim to mimic real-world systems and should therefore be driven by input models that accurately represent the behaviour of the system of interest. In most scenarios input models are estimated from observations taken from the real-world system of interest. This means they are never correct, and the error in them will propagate to the simulation output – which is what we are interested in. Quantifying the error propagated to the simulation output caused by input modelling has been a popular area of research in recent years. In this talk we will explore the problem of input modelling error, why it is important, and how it can be quantified.

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