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Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

Research Page of Markus Harva

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.

Publications

Journal articles

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
Pdf (655k)

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.
Pdf (416k)

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
Gzipped postscript (279k)

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
Pdf (1128k)

Refereed conference articles

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.
Pdf (1035k)

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.
Gzipped postscript (235k), Pdf (164k)

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.
Gzipped postscript (146k), Pdf (201k)

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.
Gzipped postscript (587k), Pdf (626k)

Other publications

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.

Theses

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.
Gzipped postscript (926k), Pdf (893k)

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