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Information Projection
In information theory, the information projection or I-projection of a probability distribution ''q'' onto a set of distributions ''P'' is :p^* = \underset \operatorname_(p, , q). where D_ is the Kullback–Leibler divergence from ''q'' to ''p''. Viewing the Kullback–Leibler divergence as a measure of distance, the I-projection p^* is the "closest" distribution to ''q'' of all the distributions in ''P''. The I-projection is useful in setting up information geometry, notably because of the following inequality, valid when ''P'' is convex: \operatorname_(p, , q) \geq \operatorname_(p, , p^*) + \operatorname_(p^*, , q). This inequality can be interpreted as an information-geometric version of Pythagoras' triangle-inequality theorem, where KL divergence is viewed as squared distance in a Euclidean space. It is worthwhile to note that since \operatorname_(p, , q) \geq 0 and continuous in p, if ''P'' is closed and non-empty, then there exists at least one minimizer to the optim ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Theory
Information theory is the scientific study of the quantification, storage, and 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, and electrical engineering. A key measure in information theory is 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 die (with six equally likely outcomes). Some other important measures in information theory are mutual information, channel capacity, error exponents, and relative entropy. Important sub-fields of information theory include sourc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probability Distribution
In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of would take the value 0.5 (1 in 2 or 1/2) for , and 0.5 for (assuming that the coin is fair). Examples of random phenomena include the weather conditions at some future date, the height of a randomly selected person, the fraction of male students in a school, the results of a survey to be conducted, etc. Introduction A probability distribution is a mathematical description of the probabilities of events, subsets of the sample space. The sample space, often denoted by \Omega, is the set of all possible outcomes of a rando ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kullback–Leibler Divergence
In mathematical statistics, the Kullback–Leibler divergence (also called relative entropy and I-divergence), denoted D_\text(P \parallel Q), is a type of statistical distance: a measure of how one probability distribution ''P'' is different from a second, reference probability distribution ''Q''. A simple interpretation of the KL divergence of ''P'' from ''Q'' is the expected excess surprise from using ''Q'' as a model when the actual distribution is ''P''. While it is a distance, it is not a metric, the most familiar type of distance: it is not symmetric in the two distributions (in contrast to variation of information), and does not satisfy the triangle inequality. Instead, in terms of information geometry, it is a type of divergence, a generalization of squared distance, and for certain classes of distributions (notably an exponential family), it satisfies a generalized Pythagorean theorem (which applies to squared distances). In the simple case, a relative entropy of 0 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Geometry
Information geometry is an interdisciplinary field that applies the techniques of differential geometry to study probability theory and statistics. It studies statistical manifolds, which are Riemannian manifolds whose points correspond to probability distributions. Introduction Historically, information geometry can be traced back to the work of C. R. Rao, who was the first to treat the Fisher matrix as a Riemannian metric. The modern theory is largely due to Shun'ichi Amari, whose work has been greatly influential on the development of the field. Classically, information geometry considered a parametrized statistical model as a Riemannian manifold. For such models, there is a natural choice of Riemannian metric, known as the Fisher information metric. In the special case that the statistical model is an exponential family, it is possible to induce the statistical manifold with a Hessian metric (i.e a Riemannian metric given by the potential of a convex function). In thi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
E-values
In statistical hypothesis testing, e-values quantify the evidence in the data against a null hypothesis (e.g., "the coin is fair", or, in a medical context, "this new treatment has no effect"). They serve as a more robust alternative to p-values, addressing some shortcomings of the latter. In contrast to p-values, e-values can deal with optional continuation: e-values of subsequent experiments (e.g. clinical trials concerning the same treatment) may simply be multiplied to provide a new, "product" e-value that represents the evidence in the joint experiment. This works even if, as often happens in practice, the decision to perform later experiments may depend in vague, unknown ways on the data observed in earlier experiments, and it is not known beforehand how many trials will be conducted: the product e-value remains a meaningful quantity, leading to tests with Type-I error control. For this reason, e-values and their sequential extension, the ''e-process'', are the fundamenta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
F-divergence
In probability theory, an f-divergence is a function D_f(P\, Q) that measures the difference between two probability distributions P and Q. Many common divergences, such as KL-divergence, Hellinger distance, and total variation distance, are special cases of f-divergence. History These divergences were introduced by Alfréd Rényi in the same paper where he introduced the well-known Rényi entropy. He proved that these divergences decrease in Markov processes. ''f''-divergences were studied further independently by , and and are sometimes known as Csiszár f-divergences, Csiszár-Morimoto divergences, or Ali-Silvey distances. Definition Non-singular case Let P and Q be two probability distributions over a space \Omega, such that P\ll Q, that is, P is absolutely continuous with respect to Q. Then, for a convex function f: , \infty)\to(-\infty, \infty/math> such that f(x) is finite for all x > 0, f(1)=0, and f(0)=\lim_ f(t) (which could be infinite), the f-divergence ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Divergence (statistics)
In information geometry, a divergence is a kind of statistical distance: a binary function which establishes the separation from one probability distribution to another on a statistical manifold. The simplest divergence is squared Euclidean distance (SED), and divergences can be viewed as generalizations of SED. The other most important divergence is relative entropy (Kullback–Leibler divergence, KL divergence), which is central to information theory. There are numerous other specific divergences and classes of divergences, notably ''f''-divergences and Bregman divergences (see ). Definition Given a differentiable manifold M of dimension n, a divergence on M is a C^2-function D: M\times M\to [0, \infty) satisfying: # D(p, q) \geq 0 for all p, q \in M (non-negativity), # D(p, q) = 0 if and only if p=q (positivity), # At every point p\in M, D(p, p+dp) is a positive-definite quadratic form for infinitesimal displacements dp from p. In applications to statistics, the manifold ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sanov's Theorem
In mathematics and information theory, Sanov's theorem gives a bound on the probability of observing an atypical sequence of samples from a given probability distribution. In the language of large deviations theory, Sanov's theorem identifies the rate function for large deviations of the empirical measure of a sequence of i.i.d. random variables. Let ''A'' be a set of probability distributions over an alphabet ''X'', and let ''q'' be an arbitrary distribution over ''X'' (where ''q'' may or may not be in ''A''). Suppose we draw ''n'' i.i.d. samples from ''q'', represented by the vector x^n = x_1, x_2, \ldots, x_n. Then, we have the following bound on the probability that the empirical measure \hat_ of the samples falls within the set ''A'': :q^n(\hat_\in A) \le (n+1)^ 2^, where * q^n is the joint probability distribution on X^n, and * p^* is the information projection of ''q'' onto ''A''. In words, the probability of drawing an atypical distribution is bounded by a function ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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