Expected mutual information, expected weight of evidence, and divergence for random processes

1974 ◽  
pp. 47-53
Author(s):  
David Bridston Osteyee ◽  
Irving John Good
Keyword(s):  
Mutual Information ◽  
Random Processes ◽  
Weight Of Evidence

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

Nonlinear Pattern Analysis of Ventricular Premature Beats by Mutual Information

1997 ◽  
Vol 36 (04/05) ◽  
pp. 257-260 ◽  
Author(s):  
H. Saitoh ◽  
T. Yokoshima ◽  
H. Kishida ◽  
H. Hayakawa ◽  
R. J. Cohen ◽  
...  
Keyword(s):  
Mutual Information ◽  
Temporal Pattern ◽  
Pattern Analysis ◽  
Autonomic Activity ◽  
Correlation Measure ◽  
Ventricular Premature Beats ◽  
Risk Of Mortality ◽  
Premature Beats ◽  
Information Values ◽  
Nonlinear Pattern

Abstract:The frequency of ventricular premature beats (VPBs) has been related to the risk of mortality. However, little is known about the temporal pattern of occurrence of VPBs and its relationship to autonomic activity. Hence, we applied a general correlation measure, mutual information, to quantify how VPBs are generated over time. We also used mutual information to determine the correlation between VPB production and heart rate in order to evaluate effects of autonomic activity on VPB production. We examined twenty subjects with more than 3000 VPBs/day and simulated ran-( dom time series of VPB occurrence. We found that mutual information values could be used to characterize quantitatively the temporal patterns of VPB generation. Our data suggest that VPB production is not random and VPBs generated with a higher value of mutual information may be more greatly affected by autonomic activity.

Data Screening Methods – Application to Differential Diagnosis in Pancreatic Pathology from Radiological Signs

1978 ◽  
Vol 17 (01) ◽  
pp. 36-40 ◽  
Author(s):  
J.-P. Durbec ◽  
Jaqueline Cornée ◽  
P. Berthezene
Keyword(s):  
Differential Diagnosis ◽  
Mutual Information ◽  
Data Processing ◽  
Screening Methods ◽  
Automatic Data ◽  
Chronic Calcifying Pancreatitis ◽  
Number Of Patients ◽  
Calcifying Pancreatitis ◽  
Pancreatic Pathology ◽  
Data Screening

The practice of systematic examinations in hospitals and the increasing development of automatic data processing permits the storing of a great deal of information about a large number of patients belonging to different diagnosis groups.To predict or to characterize these diagnosis groups some descriptors are particularly useful, others carry no information. Data screening based on the properties of mutual information and on the log cross products ratios in contingency tables is developed. The most useful descriptors are selected. For each one the characterized groups are specified.This approach has been performed on a set of binary (presence—absence) radiological variables. Four diagnoses groups are concerned: cancer of pancreas, chronic calcifying pancreatitis, non-calcifying pancreatitis and probable pancreatitis. Only twenty of the three hundred and forty initial radiological variables are selected. The presence of each corresponding sign is associated with one or more diagnosis groups.

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