001 Corpus ID: 219721347; On a family of singular continuous measures related to the doubling map @article{Baake2020OnAF, title={On a family of
Jun 1, 2021 · Request PDF | On a family of singular continuous measures related to the doubling map | Here, we study some measures that can be represented by infinite Riesz
Sep 16, 2014 · Here, we propose such an extension for the practically relevant class of singular probability measures that are supported on a lower-dimensional subset of
Abstract A multidimensional classification of singularly continuous (w
Apr 1, 2005 · Fractal properties of singular continuous probability measures with independent “ Q *-symbols” are studied in details
Aug 18, 2012 · The concept can be extended to signed measures or vector-valued measures: in this case it is required that $\mu (B\cap E) = \nu (A\cap E) = 0$ for every
Sep 1, 1993 · We introduce a new class of algebraic numbers containing the P
Nov 22, 2022 · A probability measure μ ∈ I n v (P) is called extremal if it cannot be written as μ = (1 − t)μ 0 + tμ 1 with μ 0, μ 1 ∈ I n v (P), 0 < t < 1, and μ 0 ≠ μ 1
94 (4) (1982), 313–333] introduced families of W-shaped maps that can have a
A random variable X with values in T defines a new probability space: T is the set of outcomes
This bound depends only on the number of Lorenz-like equilibria contained in the at-tracting set
Mathem
Consider the following SDE on Rd: A probability measure gives probabilities to a sets of experimental outcomes (events)
First a general probabilistic theory of the pull- of the conditional probability of P given f i
Finite-dimensional distribution
The probability “density” for P is u(x 1,x 2) = √1 2π e−x2 1 /2δ(x 2 −x 1)
The measure that assigns measure 1 to Borel sets containing an unbounded closed subset of the countable ordinals and assigns 0 to other Borel sets is a Borel probability measure that is neither inner regular nor outer regular
The concept of contiguity was formally introduced and developed by L
Title: Transport type metrics on the space of probability measures involving singular base measures
Start with Xi iid random variables such that P(Xi = 1) = p = 1 − P(Xi = 0) and define Yp = ∞ ∑ k = 1Xi 2i The law of Y1 / 2 is the A singular measure may be atomless, as is shown by the measure concentrated on the standard Cantor set which puts zero on each gap of the set and $2^{-n} P
If Bis a small neighborhood of a Aggregate data is high-level data which is acquired by combining individual-level data
If the entries of the matrix are, say, (multivariate) normally distributed, you are working with an actual probability measure, but the probability is still zero because this measure is absolutely continuous with respect