6 citations · 9 across the 3 of their papers we have counts for
8 papers
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…
Construction and Analysis of Posterior Matching in Arbitrary Dimensions via Optimal Transport
Diego A. Mesa, Rui Ma, Siva K. Gorantla +1
The posterior matching scheme, for feedback encoding of a message point lying on the unit interval over memoryless channels, maximizes mutual information for an arbitrary number of…
Measuring Sample Path Causal Influences with Relative Entropy
Gabriel Schamberg, Todd P. Coleman
We present a sample path dependent measure of causal influence between time series. The proposed causal measure is a random sequence, a realization of which enables identification…
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…
Bayesian Lasso Posterior Sampling via Parallelized Measure Transport
Marcela Mendoza, Alexis Allegra, Todd P. Coleman
It is well known that the Lasso can be interpreted as a Bayesian posterior mode estimate with a Laplacian prior. Obtaining samples from the full posterior distribution, the Bayesia…