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The value of information can never be less than zero since the decision-maker can always ignore the additional information and make a decision as if such information is not available. No other information gathering/sharing activities can be more valuable than that quantified by value of clairvoyance.
The data value metric (DVM) quantifies the useful information content of large and heterogeneous datasets in terms of the tradeoffs between the size, utility, value, and energy of the data. [13] Such methods can be used to determine if appending, expanding, or augmenting an existent dataset may improve the modeling or understanding of the ...
The expected value of including uncertainty (EVIU) compares the value of modeling uncertain information as compared to modeling a situation without taking uncertainty into account. Since the impact of uncertainty on computed results is often analysed using Monte Carlo methods, EVIU appears to be very similar to the value of carrying out an ...
Generally, information entropy is the average amount of information conveyed by an event, when considering all possible outcomes. The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication", [2] [3] and is also referred to as Shannon entropy.
The Fisher information is a way of measuring the amount of information that an observable random variable carries about an unknown parameter upon which the probability of depends. Let f ( X ; θ ) {\displaystyle f(X;\theta )} be the probability density function (or probability mass function ) for X {\displaystyle X} conditioned on the value of ...
In this context, either an information-theoretical measure, such as functional clusters (Gerald Edelman and Giulio Tononi's functional clustering model and dynamic core hypothesis (DCH) [47]) or effective information (Tononi's integrated information theory (IIT) of consciousness [48] [49] [50]), is defined (on the basis of a reentrant process ...
The Shannon information is closely related to entropy, which is the expected value of the self-information of a random variable, quantifying how surprising the random variable is "on average". This is the average amount of self-information an observer would expect to gain about a random variable when measuring it.
At the other extreme, if is a deterministic function of and is a deterministic function of then all information conveyed by is shared with : knowing determines the value of and vice versa. As a result, the mutual information is the same as the uncertainty contained in Y {\displaystyle Y} (or X {\displaystyle X} ) alone, namely the entropy of Y ...