Jonathan M. Nichols – författare

One of the main issues in communications theory is measuring the ultimate data compression possible using the concept of entropy. While differential entropy may seem to be a simple extension of the discrete case, it is a more complex measure that often requires a more careful treatment.Handbook of Differential Entropy provides a comprehensive intro

Modelling and Estimation of Damage in Structures is a comprehensiveguide to solving the type of modelling and estimation problems associated with the physics of structural damage.

Provides a model-based approach to damage identification Presents an in-depth treatment of probability theory and random processes Covers both theory and algorithms for implementing maximum likelihood and Bayesian estimation approaches Includes experimental examples of all detection and identification approaches Provides a clear means by which acquired data can be used to make decisions regarding maintenance and usage of a structure

Modelling and Estimation of Damage in Structures is a comprehensiveguide to solving the type of modelling and estimation problems associated with the physics of structural damage.

Provides a model-based approach to damage identification Presents an in-depth treatment of probability theory and random processes Covers both theory and algorithms for implementing maximum likelihood and Bayesian estimation approaches Includes experimental examples of all detection and identification approaches Provides a clear means by which acquired data can be used to make decisions regarding maintenance and usage of a structure

Modelling and Estimation of Damage in Structures is a comprehensiveguide to solving the type of modelling and estimation problems associated with the physics of structural damage. Provides a model-based approach to damage identificationPresents an in-depth treatment of probability theory and random processesCovers both theory and algorithms for implementing maximum likelihood and Bayesian estimation approachesIncludes experimental examples of all detection and identification approachesProvides a clear means by which acquired data can be used to make decisions regarding maintenance and usage of a structure

One of the main issues in communications theory is measuring the ultimate data compression possible using the concept of entropy. While differential entropy may seem to be a simple extension of the discrete case, it is a more complex measure that often requires a more careful treatment.Handbook of Differential Entropy provides a comprehensive introduction to the subject for researchers and students in information theory. Unlike related books, this one brings together background material, derivations, and applications of differential entropy.The handbook first reviews probability theory as it enables an understanding of the core building block of entropy. The authors then carefully explain the concept of entropy, introducing both discrete and differential entropy. They present detailed derivations of differential entropy for numerous probability models and discuss challenges with interpreting and deriving differential entropy. They also show how differential entropy varies as a function of the model variance. Focusing on the application of differential entropy in several areas, the book describes common estimators of parametric and nonparametric differential entropy as well as properties of the estimators. It then uses the estimated differential entropy to estimate radar pulse delays when the corrupting noise source is non-Gaussian and to develop measures of coupling between dynamical system components.

One of the main issues in communications theory is measuring the ultimate data compression possible using the concept of entropy. While differential entropy may seem to be a simple extension of the discrete case, it is a more complex measure that often requires a more careful treatment.Handbook of Differential Entropy provides a comprehensive introduction to the subject for researchers and students in information theory. Unlike related books, this one brings together background material, derivations, and applications of differential entropy.The handbook first reviews probability theory as it enables an understanding of the core building block of entropy. The authors then carefully explain the concept of entropy, introducing both discrete and differential entropy. They present detailed derivations of differential entropy for numerous probability models and discuss challenges with interpreting and deriving differential entropy. They also show how differential entropy varies as a function of the model variance. Focusing on the application of differential entropy in several areas, the book describes common estimators of parametric and nonparametric differential entropy as well as properties of the estimators. It then uses the estimated differential entropy to estimate radar pulse delays when the corrupting noise source is non-Gaussian and to develop measures of coupling between dynamical system components.