0461261 20240103212431.820160727235959.9 10.9734/BJMCS/2016/27377 20 s. P cav_un_epca*04612602231-0851175 (2016)1-20 Transfer entropy time series Kullback-Leibler divergence causality generalized normal distribution cav_un_auth*0247122 Adaptivní systémy Department of Adaptive Systems AS AS Hlaváčková-Schindler Kateřina UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0331830 Toulias T. L. BE cav_un_auth*0331831 Kitsos C. P. GR http://library.utia.cas.cz/separaty/2016/AS/hlavackova-schindler-0461261.pdf Since its introduction, transfer entropy has become a popular information-theoretic tool for detecting causal inference between two discretized random processes. By means of statistical tools we evaluate the transfer entropy of stationary processes whose continuous probability distributions are known. We study transfer entropy of processes coming from the family of γ-order generalized normal distribution. Applying Kullback-Leibler divergence we provide explicit expressions of the transfer entropy for processes which are normal, as well as for processes from the class of γ-order normal distributions. The results achieved in the paper for continuous time can be applied also to the discrete time case, concretely to the time series whose underlying process distribution is from the discussed classes. BC 2017 3 RVO:67985556 http://hdl.handle.net/11104/0261537 S 2016 cav_un_epca*0461260 British Journal of Mathematics & Computer Science 2231-0851 Roč. 17 č. 5 2016 1 20