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- Jukka Corander, Magnus Ekdahl, Timo Koski
- Data Mining and Knowledge Discovery
- 2008

Automated statistical learning of graphical models from data has attained a considerable degree of interest in the machine learning and related literature. Many authors have discussed and/or demonstrated the need for consistent stochastic search methods that would not be as prone to yield locally optimal model structures as simple greedy methods. However,… (More)

- Timo Koski
- IEEE Trans. Information Theory
- 1995

In this paper, we evaluate a method for analyzing microarray data. The method is an attempt to learn regulatory interactions between genes from gene expression data. It is based on a Bayesian network, which is a mathematical tool for modeling conditional independences between stochastic variables. We review the dynamic nature of interacting genes, and… (More)

- Jukka Corander, Timo Koski, Tatjana Pavlenko, Annika Tillander
- SMPS
- 2012

- Timo Koski, Lars-Erik Persson
- Inf. Sci.
- 1992

- M Gyllenberg, T Koski, T Lund, H G Gyllenberg
- Bulletin of mathematical biology
- 1999

In this paper we give a mathematically precise formulation of an old idea in bacterial taxonomy, namely cumulative classification, where the taxonomy is continuously updated and possibly augmented as new strains are identified. Our formulation is based on Bayesian predictive probability distributions. The criterion for founding a new taxon is given a firm… (More)

- Jukka Corander, Yaqiong Cui, Timo Koski
- Algorithmic Probability and Friends
- 2011

- Jukka Corander, Yaqiong Cui, Timo Koski, Jukka Sirén
- Statistics and Computing
- 2013

A general inductive Bayesian classification framework is introduced for data from multiple finite alphabets using predictive representations based on random urn models and generalized exchangeability. We develop a novel principle of generative supervised and semi-supervised probabilistic classification based on marginalizing simultaneous predictive… (More)

- Magnus Ekdahl, Timo Koski
- Journal of Machine Learning Research
- 2006

In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The approximated classifier is expected to have worse performance, here measured by the probability of correct classification. We present an analysis valid in… (More)