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pro vyhledávání: '"Coleman Todd P"'
Publikováno v:
BMC Neuroscience, Vol 12, Iss Suppl 1, p P43 (2011)
Externí odkaz:
https://doaj.org/article/1b191c74023042bc9dca5ace39448040
Publikováno v:
BMC Neuroscience, Vol 11, Iss Suppl 1, p P61 (2010)
Externí odkaz:
https://doaj.org/article/9cc15b75fe7e40c2a2932f907b618de9
Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g. neuroscience and climate science) domains. While these causal measures are desirable in t
Externí odkaz:
http://arxiv.org/abs/1912.10508
Autor:
Schamberg, Gabriel, Coleman, Todd P.
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 the same order as the joint process. While this assumption is often m
Externí odkaz:
http://arxiv.org/abs/1902.00580
Akademický článek
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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 channel uses. However, it in general does not always achieve any posi
Externí odkaz:
http://arxiv.org/abs/1901.02523
Autor:
Schamberg, Gabriel, Coleman, Todd P.
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 of specific patterns that give rise to high levels of causal influence
Externí odkaz:
http://arxiv.org/abs/1810.05250
Autor:
Schamberg, Gabriel, Coleman, Todd P.
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 realization of the proposed measure may be used to identify the specif
Externí odkaz:
http://arxiv.org/abs/1805.03333
The need to reason about uncertainty in large, complex, and multi-modal datasets has become increasingly common across modern scientific environments. The ability to transform samples from one distribution $P$ to another distribution $Q$ enables the
Externí odkaz:
http://arxiv.org/abs/1801.08454
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 Bayesian Lasso, confers major advantages in performance as compared to having
Externí odkaz:
http://arxiv.org/abs/1801.02106