9 citations · 18 across the 4 of their papers we have counts for
5 papers · 1 filter
Capturing and Interpreting Unique Information
Praveen Venkatesh, Keerthana Gurushankar, Gabriel Schamberg
Partial information decompositions (PIDs), which quantify information interactions between three or more variables in terms of uniqueness, redundancy and synergy, are gaining tract…
Partial Information Decomposition via Deficiency for Multivariate Gaussians
Praveen Venkatesh, Gabriel Schamberg
Bivariate partial information decompositions (PIDs) characterize how the information in a "message" random variable is decomposed between two "constituent" random variables in term…
Direct and Indirect Effects -- An Information Theoretic Perspective
Gabriel Schamberg, William Chapman, Shang-Ping Xie +1
Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g. neuroscience and cl…
On the Bias of Directed Information Estimators
Gabriel Schamberg, Todd P. Coleman
When estimating the directed information between two jointly stationary Markov processes, it is typically assumed that the recipient of the directed information is itself Markov of…
A Sample Path Measure of Causal Influence
Gabriel Schamberg, Todd P. Coleman
We present a sample path dependent measure of causal influence between two time series. The proposed measure is a random variable whose expected sum is the directed information. A…