bibtype J - Journal Article
ARLID 0461261
utime 20240103212431.8
mtime 20160727235959.9
DOI 10.9734/BJMCS/2016/27377
title (primary) (eng) Evaluating Transfer Entropy for Normal and y-Order Normal Distributions
specification
page_count 20 s.
media_type P
serial
ARLID cav_un_epca*0461260
ISSN 2231-0851
title British Journal of Mathematics & Computer Science
volume_id 17
volume 5 (2016)
page_num 1-20
keyword Transfer entropy
keyword time series
keyword Kullback-Leibler divergence
keyword causality
keyword generalized normal distribution
author (primary)
ARLID cav_un_auth*0247122
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
name1 Hlaváčková-Schindler
name2 Kateřina
institution UTIA-B
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0331830
name1 Toulias
name2 T. L.
country BE
author
ARLID cav_un_auth*0331831
name1 Kitsos
name2 C. P.
country GR
source
url http://library.utia.cas.cz/separaty/2016/AS/hlavackova-schindler-0461261.pdf
cas_special
abstract (eng) 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.
RIV BC
reportyear 2017
num_of_auth 3
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0261537
confidential S
arlyear 2016
mrcbU63 cav_un_epca*0461260 British Journal of Mathematics & Computer Science 2231-0851 Roč. 17 č. 5 2016 1 20