[HTML][HTML] Data on copula modeling of mixed discrete and continuous neural time series
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in brief, 2016 - pubmed.ncbi.nlm.nih.gov
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (" Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (" Hu et al., 2016, Joint …
[PDF][PDF] Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in Brief, 2016 - cyberleninka.org
abstract Copula is an important tool for modeling neural dependence. Recent work on
copula has been expanded to jointly model mixed time series in neuroscience (“Hu et al …
copula has been expanded to jointly model mixed time series in neuroscience (“Hu et al …
[HTML][HTML] Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in Brief, 2016 - data-in-brief.com
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (" Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (" Hu et al., 2016, Joint …
[PDF][PDF] Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in Brief, 2016 - core.ac.uk
abstract Copula is an important tool for modeling neural dependence. Recent work on
copula has been expanded to jointly model mixed time series in neuroscience (“Hu et al …
copula has been expanded to jointly model mixed time series in neuroscience (“Hu et al …
Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in brief, 2016 - researchdiscovery.drexel.edu
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
Data on copula modeling of mixed discrete and continuous neural time series.
M Hu, M Li, W Li, H Liang - Data in Brief, 2016 - europepmc.org
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in Brief, 2016 - ui.adsabs.harvard.edu
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (" Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (" Hu et al., 2016, Joint …
Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in Brief, 2016 - infona.pl
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
[HTML][HTML] Data on copula modeling of mixed discrete and continuous neural time series
M Hu, M Li, W Li, H Liang - Data in Brief, 2016 - ncbi.nlm.nih.gov
Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …