I used to work as a researcher in the Bayes group of the Adaptive Informatics Research Centre. My research interests included machine learning and Bayesian statistics, especially approximate Bayesian inference. I worked on applications in astronomical data analysis, image/video processing, and environmental modelling.

M. Harva and S. Raychaudhury (2008). Bayesian estimation of time delays
between unevenly sampled signals. *Neurocomputing* 72(1-3),
pp. 32-38.

doi:10.1016/j.neucom.2007.12.046

M. Harva (2007). A Variational EM Approach to Predictive Uncertainty. *Neural Networks* 20(4), pp. 550-558.

doi:10.1016/j.neunet.2007.04.010

M. Harva and A. Kabán (2007). Variational Learning for Rectified Factor Analysis. *Signal Processing* 87(3), pp. 509-527.

doi:10.1016/j.sigpro.2006.06.006

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T. Raiko, H. Valpola, M. Harva, and J. Karhunen (2007). Building Blocks for Variational Bayesian Learning of Latent Variable Models. *Journal of Machine Learning Research* 8(Jan), pp. 155-201.

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L. Nolan, M. Harva, A. Kabán, and S. Raychaudhury (2006). A data-driven Bayesian approach for finding young stellar populations in early-type galaxies from their UV-optical spectra. *Monthly Notices of the Royal Astronomical Society* 366(1), pp. 321-338.

doi:10.1111/j.1365-2966.2005.09868.x

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H. Valpola, M. Harva, and J. Karhunen (2004). Hierarchical Models of Variance Sources. *Signal Processing* 84(2), pp. 267-282.

doi:10.1016/j.sigpro.2003.10.014

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M. Harva and S. Raychaudhury (2006). Bayesian Estimation of Time Delays Between Unevenly Sampled Signals. In *Proc. Int. Workshop on Machine Learning for Signal Processing (MLSP'06)*, Maynooth, Ireland, pp. 111-116.

Pdf (225k)

M. Harva (2006). A Variational EM Approach to Predicting Uncertainty in Supervised Learning. In *Proc. World Congress on Computational Intelligence (WCCI'06)*, Vancouver, BC, Canada, pp. 11091-11095.

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M. Harva, T. Raiko, A. Honkela, H. Valpola, and J. Karhunen (2005). Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. In *Proc. 21st Conference on Uncertainty in Artificial Intelligence*, Edinburgh, Scotland, pp. 259-266.

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M. Harva and A. Kabán (2005). A Variational Bayesian Method for Rectified Factor Analysis. In *Proc. Int. Joint Conf. on Neural Networks (IJCNN'05)*, Montreal, Canada, pp. 185-190.

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H. Valpola, M. Harva, and J. Karhunen (2003). Hierarchical Models of Variance Sources. In *Proc. 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003)*, Nara, Japan, pp. 83-88.

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L. Nolan, A. Kaban, M. Harva, A. Benson, and S. Raychaudhury (2007). Young stellar populations in early-type galaxies in the SDSS. In M. Bureau, E. Athanassoula, B. Barbuy, editors, *International Astronomical Union Symposium No. 245, Formation and Evolution of Galaxy Bulges*.

T. Raiko, H. Valpola, M. Harva, and J. Karhunen (2006). *Building Blocks for Variational Bayesian Learning of Latent Variable Models*. Technical report E4, Helsinki University of Technology.

M. Harva and S. Raychaudhury (2005). A new Bayesian look at estimation of gravitational lens time delays. In *Abstracts RAS National Astronomy Meeting 2005*, Birmingham, UK.

Online proceedings

L. Nolan, M. Harva, A. Kabán, and S. Raychaudhury (2005). Finding young stellar populations in early-type galaxies from independent factor models of their UV-optical spectra. In *Abstracts RAS National Astronomy Meeting 2005*, Birmingham, UK.

Online proceedings

M. Harva and A. Kabán (2005). *Bayesian Inference of Independent Components from Elliptical Stellar Population Spectra*. Technical report CSR-05-1, School of Computer Science, The University of Birmingham, UK.

V. Bochko, D. Kalenova, M. Harva, and J. Parkkinen (2004). *Spectral Color Picking Technique Using Nonlinear PCA*. Technical report 89, Department of Information Technology, Lappeenranta University of Technology, Finland.

M. Harva (2008). *Algorithms for Approximate Bayesian Inference with
Applications to Astronomical Data Analysis*. Doctoral thesis, Helsinki University of Technology, Espoo, Finland.

Electronic version

M. Harva (2004). *Hierarchical Variance Models of Image Sequences*. Master's thesis, Helsinki University of Technology, Espoo, Finland.

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