The phrase "estimation data" is correct and usable in written English.
You can use it when referring to data that is based on estimations rather than actual measurements. For example, "We used the estimation data from the previous year to make our best guess at the forecast for this year."
From the entire set of 20 song and 10 ml noise stimuli, 19 songs or 9 ml noise samples were used to estimate the models' parameters (estimation data set).
We gathered crop estimation data from nine vineyards within these areas to estimate total loss.
The parameters kout,BMD (0.140 (unitless)) and γOC (0.00953 d−1) were fit to the estimation data set; γOB was fixed at a previous estimate of 0.0739.
Normally, these approaches require estimation data to train the empirical model part of a hybrid model.
In this work we present a model-centric framework for integrated simulation, parameter estimation, data reconciliation and optimisation of industrial process systems.
For example, recent developments on model-centric technologies to support plant operations based on advanced process modelling technologies opened the opportunities for performing large-scale parameter estimation – data reconciliation applications in complex dynamic industrial environments.
The proposed approach is comprehensively evaluated using six artificial benchmarks, within several outlier contamination levels, and two datasets related to process industry systems (pH neutralization and heat exchanger), whose estimation data undergo large contamination rates.
Our approach explicitly models the observation layer, which includes a heavy-tailed Student-t likelihood, and allows for a hierarchy of multiple transition layers to learn the system dynamics directly from estimation data contaminated by outliers.
In this chapter, various multitarget-multisensor tracking algorithms to handle state estimation, data association, track initialization, spatial clutter intensity estimation, debaising, and multisensor fusion in centralized/distributed/decentralized architecture are discussed in detail, including their quantitative and qualitative merits.
We demonstrate that our approach is a natural fit for sensor networks by specifying several very different classes of traditional sensor network protocols, services and applications entirely declaratively ¿ these include tree and geographic routing, link estimation, data collection, event tracking, version coherency, and localization.
Using vegetation maps based on aerial photographs and satellite images from 7 years in combination with statistical data and ground estimation data of biomass in the period 1957 2006, we were able to assess the transitions among mountain heaths and different types of forest, the displacement of the altitudinal forest line and hence the change in biomass.
