Modern physics is confronted with a large variety of complex spatial patterns. Although both spatial statisticians and statistical physicists study random geometrical structures, there has been only little interaction between the two up to now because of different traditions and languages. This v
Statistical Physics and Spatial Statistics: The Art of Analyzing and Modeling Spatial Structures and Pattern Formation
โ Scribed by Dietrich Stoyan (auth.), Klaus R. Mecke, Dietrich Stoyan (eds.)
- Book ID
- 127430362
- Publisher
- Springer
- Year
- 2000
- Tongue
- English
- Weight
- 3 MB
- Edition
- 1
- Category
- Library
- City
- Berlin; New York
- ISBN
- 3540450432
- ISSN
- 0075-8450
No coin nor oath required. For personal study only.
โฆ Synopsis
Modern physics is confronted with a large variety of complex spatial patterns. Although both spatial statisticians and statistical physicists study random geometrical structures, there has been only little interaction between the two up to now because of different traditions and languages.
This volume aims to change this situation by presenting in a clear way fundamental concepts of spatial statistics which are of great potential value for condensed matter physics and materials sciences in general, and for porous media, percolation and Gibbs processes in particular. Geometric aspects, in particular ideas of stochastic and integral geometry, play a central role throughout. With nonspecialist researchers and graduate students also in mind, prominent physicists give an excellent introduction here to modern ideas of statistical physics pertinent to this exciting field of research.
โฆ Subjects
Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences
๐ SIMILAR VOLUMES
Modern physics is confronted with a large variety of complex spatial patterns. Although both spatial statisticians and statistical physicists study random geometrical structures, there has been only little interaction between the two up to now because of different traditions and languages. This v
We focus on the problem of shape variability modeling in statistical pattern recognition. We present a nonlinear statistical model invariant to affine transformations. This model is learned on an ordinate set of points. The concept of relations between model components is also taken in account. This