JLJie Li
Jie Li
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jie Li has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Cillian Hourican
Affiliation Computational Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
1 shared publication - Johanna Gehlen
Computational Science Lab, Informatics Institute, University of Amsterdam, 1098 Amsterdam, The Netherlands
1 shared publication - Jos A. Bosch
University of Amsterdam, Amsterdcam, Netherlands
1 shared publication - Mika Kähönen
Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
1 shared publication - Olli T. Raitakari
59Centre for Population Health Research, Department of Clinical Physiology and Nuclear Medicine, InFLAMES Research Flagship, Turku University Hospital and University of Turku, Turku, Finland
1 shared publication - Pashupati Prasad Mishra
Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
1 shared publication
Latest Publications
- Open Access
Bias in O-Information Estimation
Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information–theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller–Maddow method is derived for the O-information. We find that for systems of variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information.Open AccessBias in O-Information Estimation
Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information–theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller–Maddow method is derived for the O-information. We find that for systems of variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information.