Papers 2008-- :

• Jukka Corander, Ulpu Remes and Timo Koski (2021): On the Jensen-Shannon divergence and the variation distance for categorical probability distributions, Kybernetika, Vol. 6, Nr. 6, pp. 879-907. Link

• Celia Garci'a-Pareja, Henrik Hult and Timo Koski (2021): Exact simulation of coupled Wright--Fisher diffusions, Advances in Applied Probability, Volume 53, Nr 4, pp. 923-950. Link

• Martina Favero, Henrik Hult and Timo Koski (2021): A dual process for the coupled Wright--Fisher diffusion, Journal of Mathematical Biology Volume 82, Nr 1, pp. 1-29 Link

• Erik Aurell, Magnus Ekeberg and Timo Koski (2019): On a Multilocus Wright-Fisher Model with Mutation and a Svirezhev-Shahshahani Gradient-like Selection Dynamics, arXiv preprint Link

• Daniel Berglund and Timo Koski (2019): On Probabilistic Multifactor Potential Outcome Models. Preprint Link

• Timo Koski, Brita Jung and Göran Högnäs (2018):Exit times for ARMA processes, Advances in Applied Probability, Volume 50, Issue A (Branching and Applied Probability) December 2018 , pp. 191-19. Link

• R. Matthias Geilhufe, Bart Olsthoorn, Alfredo D. Ferella, Timo Koski,Felix Kahlhoefer, Jan Conrad, and Alexander V. Balatsky (2018): Materials Informatics for Dark Matter Detection, Rapid Research Letter, Link

• Fredrik Armerin, Jonas Hallgren, T. Koski (2018): Forecasting Ranking in Harness Racing Using Probabilities Induced by Expected Positions, Applied Artificial Intelligence Link

• T. Koski, Erik Sandström, Ulf Sandström (2016): Towards field-adjusted production: Estimating research productivity from a zero-truncated distribution. Journal of Informetrics, Vol. 10, 4, Nov. 2016, pp. 1143–1152 Link

• Yaqiong Cui, Jukka Siren T. Koski, J.Corander (2016): Simultaneous Predictive Gaussian Classifiers. Journal of Classification, First on line Link

• H.Nyman, J. Pensar, H T. Koski, J.Corander (2015): Context-specific independence in graphical log-linear models, in Computational Statistics, July 2015, vol. , pp. 1-20 Link OR get pdf

• J. Pensar, H. Nyman, T. Koski, J.Corander (2015): Labeled directed acyclical graphs: a generalization of context-specific independence in directed graphical models, in Data Mining and Knowledge Discovery, March 2015, Volume 29, Issue 2, pp 503-533, Link

• H. Westerlind, K.Imrell, R. Ramanujam, K-M. Myhr, E. E. Gulowsen Celius, H. Harbo, F. Hanne, A. Bang Oturai, A. Hamsten, L. Alfredsson, T. Olsson, I. Kockum, T. Koski, and J. Hillert: Identity-by-descent mapping in a Scandinavian multiple sclerosis cohort. European Journal of Human Genetics, 2014, link

• H. Nyman, J. Pensar, T. Koski, J.Corander (2014): Stratified Graphical Models - Context-Specific Independence in Graphical Models, in Bayesian Analysis, Vol.9, pp 883-908. Link

• V. Jääskinen, Jie Xiong, J.Corander, and T.Koski (2013): Sparse Markov Chains for Sequence Data. Scandinavian Journal of Statistics,pp (online-preview) . . Link

• J. Corander, Y. Cui, & T. Koski (2013): Inductive Inference and Partition Exchangeability in Classification. pp. 91-105 in : Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Papers from the Ray Solomonoff 85th Memorial Conference. Lecture Notes in Artificial Intelligence, Dowe, David L. (Ed.)l 2013, XV. Link

• Corander, J. & Koski, T. & Pavlenko, T & Tillander, A. (2013): Bayesian Block-Diagonal Predictive Classifier for Gaussian Data. Synergies of Soft Computing and Statistics for Intelligent Data Analysis Advances in Intelligent Systems and Computing Volume 190, 2013, pp 543-551 Link

• Koski, T. & Noble, J (2012): A Review of Bayesian Networks and Structure Learning. Mathematica Applicanda (Matematyka Stosowana) Link

• Corander, J. and Xiong, J. & Cui, Y. and Koski, T. (2012): Optimal Viterbi Bayesian predictive classification for data from finite alphabets. Journal of Statistical Planning and Inference. Link

• M. Singull & Koski, T. (2012). On the Distribution of Matrix Quadratic Forms. Communications in Statistics - Theory and Methods (CIS). Link

• J. Corander, Y. Cui, T. Koski & J. Siren(2011): Have I Seen You Before ? Principles of Bayesian Predictive Classification Revisited. Statistics and Computing Link

• T. Koski, E. Sandström, & U. Sandström (2011): Estimating Research Productivity from a Zero-Truncated Distribution. PROCEEDINGS OF THE 13TH CONFERENCE OF THE INTERNATIONAL SOCIETY FOR SCIENTOMETRICS AND INFORMETRICS, VOLS 1 AND 2, Pages: 747-755. Link

• J. Corander, M. Gyllenberg & T.Koski(2011): Learning Genetic Population Structures Using Minimization of Stochastic Complexity. Entropy; Link

• J. Corander, M.Ekdahl & T.Koski (2009): Bayesian Unsupervised Learning of DNA Regulatory Binding Regions (in Advances in Artificial Intelligence 2009) Link

• J. Corander, M.Gyllenberg & T.Koski (2009): Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy (in Advances in Data Analysis and Classification 2009) Link

• J. Corander, M.Ekdahl & T.Koski (2008): Parallell interacting MCMC for learning of graph topologies (in Data Mining and Knowledge Discovery 2008) Link

Other writings :

• J. Corander, O. Diekmann & T. Koski, "A tribute to Mats Gyllenberg, on the occasion of his 60th birthday," Journal of Mathematical Biology, vol. 72, no. 4, s. 793-795, 2016.
  

• Antonin Otahal, Timo Koski, Roman Kotecky and Lucie Fajfrova. (2016): Obituary Martin Janzura. Kybernetika 52 no. 4, 661-664, 2016 Link

  
  

• KTH Roadmap on on Big Data (2014): pdf

  
  

• White Paper on Big Data (2013): pdf

  
  

• Gunnar Englund & Timo Koski: Borel-Cantelli lemmas and the law of large numbers (2009): pdf