A generalized probabilistic theory (GPT) is a general framework to describe the operational features of arbitrary physical theories. A GPT must specify...

economics Generalized probabilistic theory, a framework to describe the features of physical theories Grounded practical theory, a social science theory "GPT"...

Space Computability theory Undecidable problem Quantum circuit Generalized probabilistic theory Heaven, Douglas (6 November 2012). "Theory of everything says...

theory: the so-called Generalized Probabilistic Theories approach and the Black boxes approach. Generalized Probabilistic Theories (GPTs) are a general...

physics was the probabilistic nature of physical phenomena at atomic scales, described in quantum mechanics. The modern mathematical theory of probability...

to an analyst. From this view, Dempster–Shafer theory appears to be a generalized form of probabilistic reasoning. Statistical relational learning Bayesian...

as latent variable models, together with a measurement model; or as probabilistic models, directly modeling the probability. The latent variable interpretation...

theorists Algebraic graph theory Geometric graph theory Extremal graph theory Probabilistic graph theory Topological graph theory Graph drawing Bender &...

theory using the tools of imprecise probabilities. We call generalized possibility every function satisfying Axiom 1 and Axiom 3. We call generalized...

This is a list of topics in number theory. See also: List of recreational number theory topics Topics in cryptography Composite number Highly composite...

Possibility theory Probabilistic logic Bayes' theorem Bayesian network G. L. S. Shackle Transferable belief model Info-gap decision theory Subjective logic...

probabilistic automaton also generalizes the concepts of a Markov chain and of a subshift of finite type. The languages recognized by probabilistic automata...

develops a generalized expectancy concerning how they will do in an athletic setting. This is also termed freedom of movement. Generalized expectancies...

A probabilistic logic network (PLN) is a conceptual, mathematical and computational approach to uncertain inference. It was inspired by logic programming...

discrepancies between expected utility theory and empirical observations, concerning choice under risky (probabilistic) or uncertain circumstances. Given...

engineering and the applied sciences because it makes possible to deal with probabilistic uncertainty in the parameters of a system. In particular, PCE has been...

Introduction to potential theory. R. E. Krieger ISBN 0-88275-224-3. J. L. Doob. Classical Potential Theory and Its Probabilistic Counterpart, Springer-Verlag...

views, the probabilistic nature of quantum mechanics is not a temporary feature which will eventually be replaced by a deterministic theory, but is instead...

system Probabilistic Turing Machine Approximation algorithm Simulated annealing Ant colony optimization algorithms Game semantics Generalized game Multiple-agent...

1951 "The General and Logical Theory of Automata." pp. 1–41 in Cerebral Mechanisms in Behavior. —— 1956. "Probabilistic Logics and the Synthesis of Reliable...

into their prime factors. In abstract algebra, objects that behave in a generalized way like prime numbers include prime elements and prime ideals. A natural...

commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. Generally, probabilistic graphical models use a graph-based...

delta function (or δ distribution), also known as the unit impulse, is a generalized function on the real numbers, whose value is zero everywhere except at...

explicit formula in the additive theory of primes with applications I. The explicit formula for the Goldbach and Generalized Twin Prime Problems by Janos...

equivalent formalisms, including Markov chains, denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential...

class labels form a finite set Y defined prior to training. Probabilistic classifiers generalize this notion of classifiers: instead of functions, they are...

Probabilistic numerics is an active field of study at the intersection of applied mathematics, statistics, and machine learning centering on the concept...

Andreescu (2003)) Similarly to the Cauchy–Schwarz inequality, one can generalize Sedrakyan's inequality to random variables. In this formulation let X...

McCullagh, P.; Nelder, J. A. (January 1, 1983). "An outline of generalized linear models". Generalized Linear Models. Springer US. pp. 21–47. doi:10.1007/978-1-4899-3242-6_2...

In mathematics, dependent random choice is a probabilistic technique that shows how to find a large set of vertices in a dense graph such that every small...