On a Formula for the L2 Wasserstein Metric between Measures on Euclidean and Hilbert Spaces

For a separable metric space (X, d) L^p Wasserstein metrics between probability measures μ and v on X are defined by where the infimum is taken over all probability measures η on X × X with marginal distributions μ and v, respectively. After mentioning some basic properties of these metrics as well as explicit formulae for X = R a formula for the L² Wasserstein metric with X = Rⁿ will be cited from [5], [9], and [21] and proved for any two probability measures of a family of elliptically contoured distributions. Finally this result will be generalized for Gaussian measures to the case of a separable Hilbert space.     

Planning systems and artificial intelligence

Traditional O.R. systems are compared with problem solving in Artificial Intelligence via Expert Systems. The discussion centers on explicit knowledge representation. The general aspects are illustrated by two planning systems:

1. LESP 2: A learning system for inspection plan generation
2. IDA: A system for finding functions and solutions in construction

     

N,C10-Überbrückte Morphinalkaloide, 5H-10,13-Iminoethano-phenanthro[4,5-bcd]furan, 2. Mitt.

Summary Following our concept of rather new structural variations in the nitrogen region of morphine alkaloids we describe the first synthesis of N/C-10-bridged tertiary bases.