On exceptional sets in the metrical theory of uniform distribution
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On exceptional sets in the metrical theory of uniform distribution

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Published by typescript in [s.l.] .
Written in English


Book details:

Edition Notes

Thesis (Ph.D.) - University of Warwick, 1986.

StatementRadhakrishnan Nair.
ID Numbers
Open LibraryOL13863919M

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Chapter are pretty good for the theory of distribution. The problem is that this book is quite dry, no much motivations behind. So you might have a difficult time in the beginning. It is good to read the book Strichartz, R. (), A Guide to Distribution Theory and . Distribution of integer sequences and sequences from groups and generalized spaces. Distribution problems in finite abstract sets. Continuous uniform distribution. Discrepancies. Pseudorandom number generators. Quasi-Monte Carlo integration. Quasi-Monte Carlo methods in financial mathematics. Theory of densities. A set is pure if all of its members are sets, all members of its members are sets, and so on. For example, the set {{}} containing only the empty set is a nonempty pure set. In modern set theory, it is common to restrict attention to the von Neumann universe of pure sets, and many systems of axiomatic set theory are designed to axiomatize the pure sets only. This is a list of exceptional set mathematics, and in particular in mathematical analysis, it is very useful to be able to characterise subsets of a given set X as 'small', in some definite sense, or 'large' if their complement in X is small. There are numerous concepts that have been introduced to study 'small' or 'exceptional' subsets. In the case of sets of natural .

Theory of Distributions by J. Ian Richards and Heekyung K. Youn is a self-described "non-technical introduction", which seems to mean you don't need to know functional analysis, measure theory, or topology. But you do need to think more like a mathematician than like a physicist or engineer; it's all mathematically rigorous. It contains the authors' original results on the . Academic Journals Database is a universal index of periodical literature covering basic research from all fields of knowledge, and is particularly strong in medical research, humanities and social sciences. Full-text from most of the articles is available. Academic Journals Database contains complete bibliographic citations, precise indexing, and informative abstracts for papers from a . metrical properties of t hese exceptional sets are closely related t o fundamental re- sults i n the metrical theory of Diophantine appro x imation. The counterpart of Dio-Author: Maurice Dodson. theory. While introducing the theory we will present examples from the theory of uniform distribution in the language of dynamical systems. These will serve as illustrations and motivation. As a motivating problem, bounded remainder sets are investigated for certain constructions of low-discrepancy point sequences.

Apparently, the solution to the Card Doubling Paradox is that a uniform probability distribution over the positive real numbers doesn't exist. Can anyone explain why this is the case and what probability distributions can exist over the positive real numbers (it seems that this would be quite limited, given that such a simple distribution is. Uniform Distribution Theory 3 (), no.1, 1{18 uniform distribution theory ON WEIGHTED DISTRIBUTION FUNCTIONS OF SEQUENCES Rita Giuliano Antonini | Oto Strauch ABSTRACT. In this paper we prove that the set of logarithmically weighted distribution functions of the sequence of iterated logarithm log(i) n mod 1, n = ni;ni + 1; is the same as. FOUNDATIONS OF THE THEORY OF UNIFORM DISTRIBUTION PETE L. CLARK Introduction The book Uniform distribution of sequences by Kuipers and Niederreiter, long out of print, has recently been made available again by Dover books.1 I came across a copy at the Borders bookstore in San Francisco and decided to give it a try (the price, as they say, was. This page covers Uniform Distribution, Expectation and Variance, Proof of Expectation and Cumulative Distribution Function. A continuous random variable X which has probability density function given by: f(x) = 1 for a £ x £ b b - a (and f(x) = 0 if x is not between a and b) follows a uniform distribution with parameters a and b. We write X.