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Constraint (information Theory)
Constraint in information theory is the degree of statistical dependence between or among variables. Garner Garner W R (1962). ''Uncertainty and Structure as Psychological Concepts'', John Wiley & Sons, New York. provides a thorough discussion of various forms of constraint (internal constraint, external constraint, total constraint) with application to pattern recognition and psychology. See also * Mutual Information * Total Correlation * Interaction information The interaction information is a generalization of the mutual information for more than two variables. There are many names for interaction information, including ''amount of information'', ''information correlation'', ''co-information'', and sim ... References Information theory {{compsci-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Theory
Information theory is the scientific study of the quantification (science), quantification, computer data storage, storage, and telecommunication, communication of information. The field was originally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. The field is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering (field), information engineering, and electrical engineering. A key measure in information theory is information entropy, entropy. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. For example, identifying the outcome of a fair coin flip (with two equally likely outcomes) provides less information (lower entropy) than specifying the outcome from a roll of a dice, die (with six equally likely outcomes). Some other important measures in information theory are mutual informat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pattern Recognition
Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. These activities can be viewed as two facets of the same field of application, and they have undergone substantial development over the past few decades. Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses more on the s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Psychology
Psychology is the scientific study of mind and behavior. Psychology includes the study of conscious and unconscious phenomena, including feelings and thoughts. It is an academic discipline of immense scope, crossing the boundaries between the natural and social sciences. Psychologists seek an understanding of the emergent properties of brains, linking the discipline to neuroscience. As social scientists, psychologists aim to understand the behavior of individuals and groups.Fernald LD (2008)''Psychology: Six perspectives'' (pp.12–15). Thousand Oaks, CA: Sage Publications.Hockenbury & Hockenbury. Psychology. Worth Publishers, 2010. Ψ (''psi''), the first letter of the Greek word ''psyche'' from which the term psychology is derived (see below), is commonly associated with the science. A professional practitioner or researcher involved in the discipline is called a psychologist. Some psychologists can also be classified as behavioral or cognitive scientists. Some psyc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mutual Information
In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the " amount of information" (in units such as shannons (bits), nats or hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable. Not limited to real-valued random variables and linear dependence like the correlation coefficient, MI is more general and determines how different the joint distribution of the pair (X,Y) is from the product of the marginal distributions of X and Y. MI is the expected value of the pointwise mutual information (PMI). The quantity was defined and analyzed by Claude Shannon in his landmark paper "A Mathemati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Interaction Information
The interaction information is a generalization of the mutual information for more than two variables. There are many names for interaction information, including ''amount of information'', ''information correlation'', ''co-information'', and simply ''mutual information''. Interaction information expresses the amount of information (redundancy or synergy) bound up in a set of variables, ''beyond'' that which is present in any subset of those variables. Unlike the mutual information, the interaction information can be either positive or negative. These functions, their negativity and minima have a direct interpretation in algebraic topology. Definition The conditional mutual information can be used to inductively define the interaction information for any finite number of variables as follows: :I(X_1;\ldots;X_) = I(X_1;\ldots;X_n) - I(X_1;\ldots;X_n\mid X_), where :I(X_1;\ldots;X_n \mid X_) = \mathbb E_\big(I(X_1;\ldots;X_n) \mid X_\big). Some authors define the interaction inf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
