Mikkel N. schmidt
- Mikkel N. Schmidt, Tue
Herlau, and Morten Mørup.
Probabilistic structural hierarchical clustering of normal relational data.
In (submitted to) to Cognitive Information Processing (CIP), 2014.
- Fumiko K. Glückstad, Tue
Herlau, Mikkel N. Schmidt, and Morten
Cross-categorization of legal concepts across boundaries of legal systems.
Artificial Intelligence and Law, 2013.
- Mikkel N. Schmidt and Morten
Non-parametric bayesian modeling of complex networks. an introduction.
IEEE Signal Processing Magazine, 30(3):110–128, May 2013.
- Morten Mørup and Mikkel N.
Bayesian community detection.
Neural Computation, 24(9):2434–56, 2012.
Statistical models are used in all areas of science to describe stochastic relations between variables. In statistical modeling, probability theory is used to describe the uncertaincy that is present due to inaccurate measurements, model mismatch, missing data, etc. The process of drawing conclusions based on statistical models is called statistical inference. The aim of my research is to develop novel statistical methodology, which includes:
- Formulating probabilistic models and devising procedures for computational inference, evaluation, and validation.
- Applying the developed methodology to solve problems in various application areas in science and industry.
Tommy Sonne Alstrøm
Mads G. Chrisensen
Fumiko Kano Glückstad
Lars Kai Hansen
Søren H. Jensen
Rasmus K. Olsson,