Advancement of computationally intensive methods for efficient modern general-purpose statistical analysis and inference

Basic Info


The importance of statistical data analysis in today's world is very high. All the empirical sciences, health, finance, fraud detection, telecommunications, social networking, and marketing are just a few areas, which rely heavily on data and their analysis. While applied statistics, especially modern Bayesian statistics, have progressed tremendously and have become much more accessible, progress has recently been slowing down, because current state-of-the-art computation cannot handle the models and volumes of data we want to analyze today. The issue of inefficient statistical computation has recently been highlighted as one of the top 5 open problems in statistics. The primary objective of the project was to contribute to solving this problem by researching an approach to more efficient general-purpose computation and implementing the findings in a tool, which would allow us to analyze ever growing volumes of data at a reasonable cost.

Anton Melik Geographical Institute ZRC SAZU was cooperating in the frame of the project by testing and solving gegraphical research issues with new quantitative methods developed by main project leader Faculty of Computer and Information Science, University of Ljubljana. Our institute prepared numerous studies, which were presented in international journals and conferences:

BREG VALJAVEC, M., CIGLIČ, R., OŠTIR, K., RIBEIRO, D. 2018: Modelling habitats in karstland scape by integrating remote sensing and topography data. Open geosciences 10. DOI: 10.1515/geo-2018-0011 [COBISS.SI-ID 43194413]
CIGLIČ, R. 2017: Landscape classification with quantitative methods. Evaluating raster data layers according to the scale of classificatio. Lecture at Ss. Cyril and Methodius University, Faculty of Natural Sciences and Mathematics, Institute of Geography, Skopje (Makedonija). 22. maj 2017. [COBISS.SI-ID 41589293]CIGLIČ, R., PERKO, D. 2017: A method for evaluating raster data layers according to landscape classification scale. Ecological informatics 39. DOI: 10.1016/j.ecoinf.2017.03.004. [COBISS.SI-ID 41426477]
CIGLIČ, R. 2018: Assessing the impact of input data incongruity in selected quantitative methods for modelling natural landscape typologies. Geografski vestnik 90-1. DOI: 10.3986/GV90107 [COBISS.SI-ID 44351021]
CIGLIČ, R. 2018: Evaluating landscape classifications with machine learning: the case of Slovenia. Conference presentation at 4th International Scientific Conference Geobalcanica 2018 "Connect all geographers!", Ohrid (Makedonija), 15. maj 2018. [COBISS.SI-ID 43886893]
CIGLIČ, R., PERKO, D., HRVATIN, M., ŠTRUMBELJ, E. 2017: Modeling and evaluating older landscape classifications with modern quantitative methods. From pattern and process to people and action. Ghent: IALE-Europe. 2017. [COBISS.SI-ID 41978413]
CIGLIČ, R., ŠTRUMBELJ, E., ČEŠNOVAR, R., HRVATIN, M., PERKO, D. 2019: Evaluating existing manually constructed natural landscape classification with a machine learning-based approach. Journal of spatial information science18. DOI: 10.5311/JOSIS.2019.18.464 [COBISS.SI-ID 44825901]