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https://math.stackexchange.com/questions/660085/is-it-possible-to-construct-a-probability-distribution-for-a-given-compact-sup
$\begingroup$ If what you mean is that you have a priori an arbitrary series of decreasing numbers between $1$ and $0$, and you want these numbers to represent the moments of a distribution, then, no I don't think you can find one -except if they were actually generated from the MGF of a distribution, and you are called to discover it (and hence the distribution).
https://www.academia.edu/6224784/Compact_support_probability_distributions_in_random_matrix_theory
Compact support probability distributions in random matrix theory
https://pdfs.semanticscholar.org/ab4b/f2aa9c90a6ed655b517888e1fa8474501ebf.pdf
SAMPLING FROM A LOG-CONCAVE DISTRIBUTION WITH COMPACT SUPPORT The proof of Theorem2follows from combining Proposition6and Proposition4below. Note that these two results imply explicit bounds between Rn and ˇfor all n2N and >0. The problem of sampling from a probability measure restricted to a convex compact support
https://arxiv.org/pdf/1705.08964.pdf
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo Nicolas Brosse 1 Alain Durmus 2 Eric Moulines 3 Marcelo Pereyra 4 Abstract This paper presents a detailed theoretical analysis of the Langevin Monte
https://www.statlect.com/glossary/support-of-a-random-variable
Support of random vectors and random matrices. The same definition applies to random vectors. If is a random vector, its support is the set of values that it can take. The concept extends in the obvious manner also to random matrices. Synonyms. The support is sometimes also called range. More details
https://en.wikipedia.org/wiki/Distribution_(mathematics)
Distribution of compact support. It is also possible to define the convolution of two distributions S and T on R n, provided one of them has compact support. Informally, in order to define S∗T where T has compact support, the idea is to extend the definition of the convolution ∗ to a linear operation on distributions so that the ...
https://en.wikipedia.org/wiki/Probability_distribution
A continuous probability distribution is a probability distribution with a cumulative distribution function that is absolutely continuous. Equivalently, it is a probability distribution on the real numbers that is absolutely continuous with respect to Lebesgue measure. Such distributions can be represented by their probability density functions.
https://www.sciencedirect.com/science/article/pii/S1386947709002422
Phase-space representations of quantum distributions such as the Wigner function and Kadanoff–Baym Green's functions do not have compact support which leads to difficulties for numerical and perturbative schemes in quantum transport simulations of nano-devices.
http://www.math.chalmers.se/~hasse/distributioner_eng.pdf
6 Distributions with compact support 31 7 Convergence of distributions 32 8 Convolution of distributions 36 9 Fundamental solutions 43 10 The Fourier transform 47 11 The Fourier transform on L2 55 12 The Fourier transform and convolutions 57 13 The Paley-Wiener theorem 63
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