Mutual information for locally infinitely divisible random processes

1974 ◽  
Vol 14 (1) ◽  
pp. 1-6 ◽  
Author(s):  
B. Grigelionis
Keyword(s):  
Mutual Information ◽  
Random Processes ◽  
Infinitely Divisible

Learning Minimal Latent Directed Information Polytrees

2016 ◽  
Vol 28 (9) ◽  
pp. 1723-1768 ◽  
Author(s):  
Jalal Etesami ◽  
Negar Kiyavash ◽  
Todd Coleman
Keyword(s):  
Mutual Information ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Random Processes ◽  
Learning Task ◽  
Sample Complexity ◽  
Empirical Estimator ◽  
Discrepancy Measure ◽  
Directed Information ◽  
New Type

We propose an approach for learning latent directed polytrees as long as there exists an appropriately defined discrepancy measure between the observed nodes. Specifically, we use our approach for learning directed information polytrees where samples are available from only a subset of processes. Directed information trees are a new type of probabilistic graphical models that represent the causal dynamics among a set of random processes in a stochastic system. We prove that the approach is consistent for learning minimal latent directed trees. We analyze the sample complexity of the learning task when the empirical estimator of mutual information is used as the discrepancy measure.

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function

The psychophysics of random processes

1976 ◽  
Author(s):  
Renwick E. Curry ◽  
T. Govindaraj
Keyword(s):  
Random Processes
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