Parametric statistics

TL;DR: A data-driven approach to the performance analysis of wind turbines is presented and the power curve model constructed by the least squares method outperforms the one built by the maximum likelihood approach.

TL;DR: A versatile framework for synthesis of simple, yet well-performing control strategies that mimic the behavior of optimization-based controllers, also for large scale multiple-input-multiple-output (MIMO) control problems which are common in the building sector.

TL;DR: In this article, the uniqueness of seminal parametric design concepts and their impact on models of parametric Design Thinking (PDT) are examined through review of key texts and theoretical concepts from early cognitive models up to current models.

TL;DR: This work uses a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media and finds that the dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA, and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model.