EigenfunctionIn mathematics, an eigenfunction of a linear operator D defined on some function space is any non-zero function in that space that, when acted upon by D, is only multiplied by some scaling factor called an eigenvalue. As an equation, this condition can be written as for some scalar eigenvalue The solutions to this equation may also be subject to boundary conditions that limit the allowable eigenvalues and eigenfunctions. An eigenfunction is a type of eigenvector.
Nonprobability samplingSampling is the use of a subset of the population to represent the whole population or to inform about (social) processes that are meaningful beyond the particular cases, individuals or sites studied. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. In cases where external validity is not of critical importance to the study's goals or purpose, researchers might prefer to use nonprobability sampling.
FallacyA fallacy, also known as paralogia in modern psychology, is the use of invalid or otherwise faulty reasoning in the construction of an argument that may appear to be well-reasoned if unnoticed. The term was introduced in the Western intellectual tradition by the Aristotelian De Sophisticis Elenchis. Fallacies may be committed intentionally to manipulate or persuade by deception, unintentionally because of human limitations such as carelessness, cognitive or social biases and ignorance, or potentially due to the limitations of language and understanding of language.
Particle in a spherically symmetric potentialIn quantum mechanics, a particle in a spherically symmetric potential is a system with a potential that depends only on the distance between the particle and a center. This is how isolated atoms are described, and plays a central role as a first approximation to the formation of chemical bonds. In the general case, the dynamics of a particle in a spherically symmetric potential are governed by a Hamiltonian of the following form: where is the mass of the particle, is the momentum operator, and the potential depends only on , the modulus of the radius vector.
Inductive programmingInductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints. Depending on the programming language used, there are several kinds of inductive programming.
Solomonoff's theory of inductive inferenceSolomonoff's theory of inductive inference is a mathematical theory of induction introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes' rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory.
Padovan sequenceIn number theory, the Padovan sequence is the sequence of integers P(n) defined by the initial values and the recurrence relation The first few values of P(n) are 1, 1, 1, 2, 2, 3, 4, 5, 7, 9, 12, 16, 21, 28, 37, 49, 65, 86, 114, 151, 200, 265, ... A Padovan prime is a Padovan number that is prime. The first Padovan primes are: 2, 3, 5, 7, 37, 151, 3329, 23833, 13091204281, 3093215881333057, 1363005552434666078217421284621279933627102780881053358473, 1558877695141608507751098941899265975115403618621811951868598809164180630185566719, .
Baconian methodThe Baconian method is the investigative method developed by Sir Francis Bacon, one of the founders of modern science, and thus a first formulation of a modern scientific method. The method was put forward in Bacon's book Novum Organum (1620), or 'New Method', and was supposed to replace the methods put forward in Aristotle's Organon. This method was influential upon the development of the scientific method in modern science; but also more generally in the early modern rejection of medieval Aristotelianism.
Algorithmic probabilityIn algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the method together with Bayes' rule to obtain probabilities of prediction for an algorithm's future outputs.
