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Ola Hössjer
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Journal Articles
- 2024
- [j15]Steinar Thorvaldsen
, Ola Hössjer:
Use of directed quasi-metric distances for quantifying the information of gene families. Biosyst. 243: 105256 (2024) - [j14]Måns Karlsson, Ola Hössjer
Classification Under Partial Reject Options. J. Classif. 41(1): 2-37 (2024) - [j13]Måns Karlsson, Ola Hössjer
Erratum: Classification Under Partial Reject Options. J. Classif. 41(1): 38 (2024) - [j12]Ola Hössjer
An Information Theoretic Approach to Prevalence Estimation and Missing Data. IEEE Trans. Inf. Theory 70(5): 3567-3582 (2024) - 2022
- [j11]Daniel Andrés Díaz-Pachón
Assessing, Testing and Estimating the Amount of Fine-Tuning by Means of Active Information. Entropy 24(10): 1323 (2022) - [j10]Ola Hössjer
A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning. Entropy 24(10): 1469 (2022) - 2019
- [j9]Ola Hössjer
A spatio-temporal point process model for particle growth. J. Appl. Probab. 56(1): 23-38 (2019) - 2015
- [j8]Magnus Lekman, Robert Karlsson, Lisette Graae, Ola Hössjer
A significant risk locus on 19q13 for bipolar disorder identified using a combined genome-wide linkage and copy number variation analysis. BioData Min. 8: 42 (2015) - 2014
- [j7]Magnus Lekman, Ola Hössjer
The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study. BioData Min. 7: 19 (2014) - 2009
- [j6]Linda Hartman
Utilizing identity-by-descent probabilities for genetic fine-mapping in population based samples, via spatial smoothing of haplotype effects. Comput. Stat. Data Anal. 53(5): 1802-1817 (2009) - 2008
- [j5]Linda Hartman
Fast kriging of large data sets with Gaussian Markov random fields. Comput. Stat. Data Anal. 52(5): 2331-2349 (2008) - 2006
- [j4]Henrik Bengtsson
Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method. BMC Bioinform. 7: 100 (2006) - 1997
- [j3]Rolf T. Gustafsson, Ola G. Hössjer
Adaptive detection of known signals in additive noise by means of kernel density estimators. IEEE Trans. Inf. Theory 43(4): 1192-1204 (1997) - 1995
- [j2]Ola Hössjer
On-line density estimators with high efficiency. IEEE Trans. Inf. Theory 41(3): 829-833 (1995) - 1993
- [j1]Ola Hössjer
Robust multiple classification of known signals in additive noise - An asymptotic weak signal approach. IEEE Trans. Inf. Theory 39(2): 594-608 (1993)
Informal and Other Publications
- 2025
- [i4]Daniel Andrés Díaz-Pachón, H. Renata Gallegos, Ola Hössjer, J. Sunil Rao:
Statistical learning does not always entail knowledge. CoRR abs/2501.01963 (2025) - 2024
- [i3]Daniel Andrés Díaz-Pachón, Ola Hössjer, Calvin Mathew:
Is it possible to know cosmological fine-tuning? CoRR abs/2401.04190 (2024) - 2022
- [i2]Ola Hössjer, Daniel Andrés Díaz-Pachón, Chen Zhao, J. Sunil Rao:
Active information, missing data and prevalence estimation. CoRR abs/2206.05120 (2022) - [i1]Daniel Andrés Díaz-Pachón, Ola Hössjer:
Assessing, testing and estimating the amount of fine-tuning by means of active information. CoRR abs/2208.13828 (2022)
Coauthor Index
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