Self-Potential Method: Theoretical Modeling And Applications In Geosciences

Diesen und anderen Fragen widmen sich die Beiträge des Bandes aus Sicht der Volkskunde, Soziologie, Literaturwissenschaft, Narratologie, Fremdsprachendidaktik, Geschichtsdidaktik, Physikdidaktik, Heil- und Sonderpädagogik, Grundschulpädagogik sowie der Erziehungswissenschaft. Wie können sich Lernende das Erfahrene durch eigenes Erzählen erschließen, und wie verknüpfen und verändern sie ihre sowie fremde Erfahrungen über das Erzählen? In welchem Verhältnis stehen Lernen und Erzählen?.

The magnetic anomaly model and surface topography (from the LOLA mission) are added. Atlas of the Gravity and Magnetic Fields of the Moon.Results are shown for the whole Moon, for its segments around the Moon and as zooms for selected impact craters, maria and catenae; they are shortly interpreted and opened for further selenologic applications. This book presents gravity aspects (gravity disturbance, Marussi tensor, two gravity invariants, their certain ratio, the strike angles, and the virtual deformations) which are computed and evaluated for the Moon, using the recent static global gravity field model (GRGM1200A) to degree and order of 600 in spherical harmonic expansion.

This book presents gravity aspects (gravity disturbance, Marussi tensor, two gravity invariants, their certain ratio, the strike angles, and the virtual deformations) which are computed and evaluated for the Moon, using the recent static global gravity field model (GRGM1200A) to degree and order of 600 in spherical harmonic expansion.Results are shown for the whole Moon, for its segments around the Moon and as zooms for selected impact craters, maria and catenae; they are shortly interpreted and opened for further selenologic applications. The magnetic anomaly model and surface topography (from the LOLA mission) are added.

This second edition adds three new chapters: (1) Chapter on integer least squares that covers (i) model for positioning as a mixed integer linear model which includes integer parameters. (iv) The famous LLL algorithm for generating a Lovasz reduced basis is explained. Chapter seven is a speciality in the treatment of an overjet.

(2) Bayes methods that covers (i) general principle of Bayesian modeling. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss–Markov model and a least squares solution (LESS) in a system of linear equations. Applications of Linear and Nonlinear Models (3) Error-in-variables models, which cover: (i) Introduce the error-in-variables (EIV) model, discuss the difference to least squares estimators (LSE), (ii) calculate the total least squares (TLS) estimator. Our last chapter is devoted to probabilistic regression, the special Gauss–Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation.