Discrete Gaussian Distributions via Theta Functions
From MaRDI portal
Publication:4634347
DOI10.1137/18M1164937zbMath1425.60020arXiv1801.02373WikidataQ128471572 ScholiaQ128471572MaRDI QIDQ4634347
Carlos Améndola, Daniele Agostini
Publication date: 8 May 2019
Published in: SIAM Journal on Applied Algebra and Geometry (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1801.02373
Entropy and other invariants (28D20) Theta functions and abelian varieties (14K25) Statistical aspects of information-theoretic topics (62B10) Distribution theory (60E99)
Related Items (7)
Total positivity in exponential families with application to binary variables ⋮ Nonlinear algebra and applications ⋮ The Kullback-Leibler divergence between lattice Gaussian distributions ⋮ On computing high-dimensional Riemann theta functions ⋮ Statistical reconstruction of the GFF and KT transition ⋮ Moment identifiability of homoscedastic Gaussian mixtures ⋮ Lozenge tilings and the Gaussian free field on a cylinder
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Lectures on algebraic statistics
- Characterizations of a discrete normal distribution
- Tata lectures on theta. I: Introduction and motivation: Theta functions in one variable. Basic results on theta functions in several variables. With the assistance of C. Musili, M. Nori, E. Previato, and M. Stillman
- Exponential varieties
- Trapdoors for Lattices: Simpler, Tighter, Faster, Smaller
- Solving the Shortest Vector Problem in 2 n Time Using Discrete Gaussian Sampling
- Moment Varieties of Gaussian Mixtures
- Lattice problems in NP ∩ coNP
- Trapdoors for hard lattices and new cryptographic constructions
- Note on the generation of most probable frequency distributions
- The k - Very Ampleness and k - Spannedness on Polarized Abelian Surfaces
- Probability Theory
- Algebraic Identifiability of Gaussian Mixtures
- Relationships Between Central Moments and Cumulants, with Formulae for the Central Moments of Gamma Distributions
- Computing Riemann theta functions
- An Inequality for Gaussians on Lattices
- Worst‐Case to Average‐Case Reductions Based on Gaussian Measures
- Elements of Information Theory
- On lattices, learning with errors, random linear codes, and cryptography
- Discrete normal distribution and its relationship with Jacobi theta functions
This page was built for publication: Discrete Gaussian Distributions via Theta Functions