About: Randomness is a(n) research topic. Over the lifetime, 10725 publication(s) have been published within this topic receiving 252954 citation(s).
Papers published on a yearly basis
Abstract: This paper presents a simple model for such processes as spin diffusion or conduction in the "impurity band." These processes involve transport in a lattice which is in some sense random, and in them diffusion is expected to take place via quantum jumps between localized sites. In this simple model the essential randomness is introduced by requiring the energy to vary randomly from site to site. It is shown that at low enough densities no diffusion at all can take place, and the criteria for transport to occur are given.
01 Jan 2019
TL;DR: The book presents a thorough treatment of the central ideas and their applications of Kolmogorov complexity with a wide range of illustrative applications, and will be ideal for advanced undergraduate students, graduate students, and researchers in computer science, mathematics, cognitive sciences, philosophy, artificial intelligence, statistics, and physics.
Abstract: The book is outstanding and admirable in many respects. ... is necessary reading for all kinds of readers from undergraduate students to top authorities in the field. Journal of Symbolic Logic Written by two experts in the field, this is the only comprehensive and unified treatment of the central ideas and their applications of Kolmogorov complexity. The book presents a thorough treatment of the subject with a wide range of illustrative applications. Such applications include the randomness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computational learning theory, the complexity of algorithms, and the thermodynamics of computing. It will be ideal for advanced undergraduate students, graduate students, and researchers in computer science, mathematics, cognitive sciences, philosophy, artificial intelligence, statistics, and physics. The book is self-contained in that it contains the basic requirements from mathematics and computer science. Included are also numerous problem sets, comments, source references, and hints to solutions of problems. New topics in this edition include Omega numbers, KolmogorovLoveland randomness, universal learning, communication complexity, Kolmogorov's random graphs, time-limited universal distribution, Shannon information and others.
Abstract: This paper focuses on developing and adapting statistical models of counts (nonnegative integers) in the context of panel data and using them to analyze the relationship between patents and R & D expenditures. Since a variety of other economic data come in the form of repeated counts of some individual actions or events, the methodology should have wide applications. The statistical models we develop are applications and generalizations of the Poisson distribution. Two important issues are (i) Given the panel nature of our data, how can we allow for separate persistent individual (fixed or random) effects? (ii) How does one introduce the equivalent of disturbances-in-the-equation into the analysis of Poisson and other discrete probability functions? The first problem is solved by conditioning on the total sum of outcomes over the observed years, while the second problem is solved by introducing an additional source of randomness, allowing the Poisson parameter to be itself randomly distributed, and compounding the two distributions. Lastly, we develop a test statistic for the presence of serial correlation when fixed effects estimators are used in nonlinear conditional models.
01 Jan 1921
Abstract: Part 1 Fundamental ideas: the meaning of probability - probability in relation to the theory of knowledge - the measurement of probabilities - the principle of indifference - other methods of determining probabilities - the weight of arguments - historical retrospect - the frequency theory of probability - the constructive theory of part 1 summarized. Part 2 Fundamental theorems: introductory - the theory of groups, with special reference to logical consistence, inference, and logical priority - the definitions and axioms of inference and probability - the fundamental theorems of probable inference - numerical measurement and approximation of probabilities - observations on the theorems of chapter 14 and their developments, including testimony - some problems in inverse probability, including averages. Part 3 Induction and analogy: introduction - the nature of argument by analogy - the value of multiplication of instances, or pure induction - the nature of inductive argument continued - the justification of these methods - some historical notes on induction - notes on part 3. Part 4 Some philosophical applications of probability: the meanings of objective chance, and of randomness - some problems arising out of the discussion of change - the application of probability to conduct. Part 5 The foundations of statistical inference: the nature of statistical inference - the law of great numbers - the use of a priori probabilities for the prediction of statistical frequency - the mathematical use of statistical frequencies for the determination of probability a posteriori - the inversion of Bernoulli's theorem - the inductive use of statistical frequencies for the determination of probability a posteriori - outline of a constructive theory.
•01 Jan 2005
TL;DR: Preface 1. Events and probability 2. Discrete random variables and expectation 3. Moments and deviations 4. Chernoff bounds 5. Balls, bins and random graphs 6. Probabilistic method 7. Markov chains and random walks 8. Continuous distributions and the Poisson process
Abstract: Preface 1. Events and probability 2. Discrete random variables and expectation 3. Moments and deviations 4. Chernoff bounds 5. Balls, bins and random graphs 6. The probabilistic method 7. Markov chains and random walks 8. Continuous distributions and the Poisson process 9. Entropy, randomness and information 10. The Monte Carlo method 11. Coupling of Markov chains 12. Martingales 13. Pairwise independence and universal hash functions 14. Balanced allocations References.