Workshop on Stein’s method and networks
Oxford June 20-21 2024 in person and online
Networks have emerged as useful tool to represent and analyse complex data sets. These data sets appear in many contexts - for example, biological networks are used to represent the interplay of agents within a cell, social networks represent interactions between individuals or social entities such as websites referring to other websites, trade networks reflect trade relationships between countries.
Due to the complexity of the data which they represent, networks pose considerable obstacles for analysis. Typically, the standard statistical framework of independent observations no longer applies - networks are used to represent the data precisely because they are often not independent of each other. While each network itself can be viewed as an observation, usually there are no independent observations of the whole network available.
In order to analyse such objects and similar random structures with dependence, Stein’s method is a powerful tool. Through Stein’s method it is possible to obtain explicit bounds on distributional distances. Stein’s method is also at the foundation of kernelised Stein discrepancies and related goodness-of-fit tests.
This two-day workshop will bring together researchers from Stein’s method and related areas. There will be invited speakers as well as poster sessions. It is possible to follow the talks remotely (but not the poster sessions, nor to present a poster). The in-person registration fee is £25 and includes refreshments. Online-only registration is free. The deadline for registration is June 15. There are only a limited number of in-person places available, and these are allocated on a first-come first-serve basis.
Please use the following link for more information and to register for the event.
https://sites.google.com/view/workshop-stein-networks/home?authuser=0
This workshop is made possible through funding from EPSRC grant EP/T018445/1 and is supported also by the Bernoulli Society Subject Area Committee on Statistical Network Science.
Thank you for your consideration
Gesine Reinert, Adrian Fischer, Tara Trauthwein and Beverley Lane
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