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Kristoffer Spricer

Doktorand

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Works at Department of Mathematics (incl. Math. Statistics)
Telephone 08-674 70 43
Email spricer@math.su.se
Visiting address Roslagsvägen 101, Kräftriket, hus 6
Room 309
Postal address Matematiska institutionen 106 91 Stockholm

About me

I am a PhD student in the field of mathematic statistics. The subject for my thesis is epidemics in structured populations.

I started studying mathematical statistics in 2006 and was accepted for a PhD position at Stockholm University in 2012. My supervisors are Tom Britton and Pieter Trapman. I also have a previous degree in physics from 1989. Afterwards I worked in the fields of telecom, datacom and medical devices for about twenty years. Part of my undergraduate and advanced level studies in mathematical statistics were done while I was still working full time.

Teaching

Year Course Duties
2012 Stochastic Processes and Simulation I Assistant teacher (exercise sessions and laboratory exercises)
2013

Applied Statistical Analysis

Assistant teacher (exercise sessions)
2014 Stochastic Processes and Simulation I Lecturer (and exercise sessions)
2015 Probability Theory I Assitant teacher (exercise sessions and laboratory exercises)
Stochastic Processes and Simulation I Lecturer
2016 Stochastic Processes and Simulation I Lecturer
2017 Stochastic Processes and Simulation Lecturer

 

Research

  • How well does the assumed exponential spread of epidemics really fit empirical networks? This is my current research area together with Pieter Trapman.

Publications

A selection from Stockholm University publication database
  • 2015. Kristoffer Spricer, Tom Britton. Journal of statistical physics 161 (4), 965-985

    The configuration model was originally defined for undirected networks and has recently been extended to directed networks. Many empirical networks are however neither undirected nor completely directed, but instead usually partially directed meaning that certain edges are directed and others are undirected. In the paper we define a configuration model for such networks where vertices have in-, out-, and undirected degrees that may be dependent. We prove conditions under which the resulting degree distributions converge to the intended degree distributions. The new model is shown to better approximate several empirical networks compared to undirected and completely directed networks.

Show all publications by Kristoffer Spricer at Stockholm University

Last updated: February 23, 2018

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