Abstract
Lombardi et al. give a comprehensive explanation of Shannon information. The question asked in their overview is: what is Shannon information? This chapter’s approach seems useful in measuring or understanding compressed sensing and a prelude on Shannon’s theorem is a necessary preliminary. Lombardi relies on numerous references: we highlight the work of Bell, Fisher, Khinchin, Kolmogorov and MacKay on data in general, without forgetting the depth of insight developed by Landauer.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Paper version not known
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
