Prosjekt- og masteroppgaver tilbudt av Sara Martino


My field of research is Computational statistics, and I have long experience in developing and working with the INLA software, see the R-INLA page.

The projects I offer are linked or motivated by an application. The goal can either be answer a concrete question from a dataset using some statistical model, or develop a new statistical method with an application as motivation. In this last case one could use simulated data instead of observed ones.

Below is a list of suggestions. Some are just ideas from collaborators so not yet very concrete. Get in touch if you find something interesting or if you have you own idea, so we can discuss possible projects together.

Analysing bycicle accidents on road networks: ReCyCLIST

In the research project ReCyCLIST (, the Transport Economics Institute and Sørlandet Hospital collaborate to collect data on accidents on bicycles, electric scooters and other forms of micromobility. People who come into contact with the healthcare system after injuring themselves on a bicycle etc. are asked to answer questions about the accident via an online questionnaire. The questionnaire has a map function where the accidents are located with coordinates.

It is of interest to investigate the spatial distribution of the accidents in order to understand more about critical points and imporant street characteristics which might increase or decrease the risk of accidents. The interesting thing is that the road networks is not continuous in space but lives on a metric graph.

Interpolation of climate variables

The global climate is undergoing significant changes, particularly noticeable in monthly mean temperatures. The Norwegian meteorological observational network has been monitoring near-surface atmospheric conditions for several decades, and in some instances, since as early as 1900. Among the various parameters tracked, monthly aggregated temperatures exhibit the most pronounced variations over time. However, these variations differ across different regions, making it crucial to advance research aimed at characterizing climate conditions and temporal trends through a variety of methodologies.

This research project intends to employ the integrated nested Laplace approximation (INLA) methodology and stochastic partial differential equations (SPDE) to analyze monthly temperature data across Norway. The resulting climatological findings will be compared with existing data to enhance our understanding of climate patterns. The primary focus of this work is to apply statistical methods in the field of climate services, with a particular emphasis on techniques used to characterize climate change.

This is a project in collaboration with

Supporting courses

The most important supporting courses for working within computational statistics are TMA4300 Computational statistics and TMA4250 Spatial statistics, but other statistics courses are also useful.

Spring semester:

Autumn semester:

Previous Master students

  • Sara Elise Wøllo (2022), "Correcting for under-reporting of violence against women in Italy using INLA".
  • Julie Berg (2022), "Multistate models in survival analysis using INLA, applied on data for resuscitation after cardiac arrest".
  • Helene Behrens (2021) "Bayesian Mortality Modeling with Linearized Integrated Nested Laplace Approximation".
  • Marion Helen Røed (2021), "Spatial Extreme Value Modelling of Sea Level Data from the Oslo Fjord"
  • August Sørli Mathisen (2020), " Inference on extreme hourly precipitation in Norway using INLA"
  • Martin Outzen Berild (2020), "Integrated Nested Laplace Approximations within Monte Carlo Methods"
  • Sigrid Leithe (2019) “Statistical Methods for the Analysis of Data with a Lower Limit of Detection”
  • Anne Siri Fardal (2019), "A Bayesian Model for Prediction of Heat Consumption"
  • Johan Øvstebø Birketvedt (2019), "Interval Censored Regular Vines with Application to Event-Based

Modelling of Precipitation and Temperature"

2023-11-20, Sara Martino