Information Geometry: Near Randomness and Near IndependenceThis volume uses information geometry to give a common differential geometric framework for a wide range of illustrative applications including amino acid sequence spacings, cryptology studies, clustering of communications and galaxies, and cosmological voids. |
Contents
| 1 | |
Introduction to Riemannian Geometry | 19 |
Information Geometry | 31 |
Information Geometry of Bivariate Families | 55 |
group headed by Andrew Doig of the Manchester Interdisciplinary Biocentre | 76 |
Neighbourhoods of Poisson Randomness Independence | 108 |
Cosmological Voids and Galactic Clustering | 119 |
Other editions - View all
Information Geometry: Near Randomness and Near Independence Khadiga Arwini,C. T. J. Dodson No preview available - 2008 |
Information Geometry: Near Randomness and Near Independence Khadiga Arwini,C. T. J. Dodson No preview available - 2008 |
Common terms and phrases
3-manifold a-connections a-curvature tensor a-Ricci tensor a-scalar curvature affine connection affine immersion amino acids Arwini bivariate gamma distribution bivariate Gaussian C.T.J. Dodson clustering coefficient of variation components are zero correlation coefficient corresponds covariance defined density func distances entropy equation exponential distribution exponential family fibre networks Fisher information metric Fisher metric Freund galaxies gamma density functions gamma distribution gamma manifold Gaussian density functions geodesic given independent components information geometry linear connections log-Gaussian marginal density functions marginal functions maximum likelihood McKay bivariate gamma mutually dual n-manifold natural coordinate system neighbourhood parameter space points Poisson process Poisson random polygon pore porous media potential function probability density function properties Proposition random variables Riemannian manifold sample scalar curvature sequence smooth standard deviation stochastic structure submanifold tangent space tangent vector tions unit mean variance vector field void