Estimation of conditional CDFs using machine learning

This paper presents a novel approach for the estimation of conditional multivariate cumulative distribution functions (CDFs) within a nonparametric framework. To achieve this, we introduce a binary random variable that indirectly represents conditional CDFs and construct a dataset by pairing input vectors with the binary variables. We developed...

Machine learning study on performance prediction of asphalt mixtures under Dutch conditions

The Netherlands is a northwestern European country. The location makes the Netherlands an important entry for trade to the European continent resulting in high traffic intensities [1]. The Netherlands has one of the densest road networks in the world, having over 139.000km of roadways [2, 3]. The understanding of the link between the functional...

Energy Study of Drying: Using Machine Learning to Predict the Energy Consumption of an Industrial Powder Drying Process

In this thesis, we use data science / statistical techniques to better understand the energy consumption behind a powder drying facility located in Zwolle, as part of Abbott's initiative to better manage its energy consumption. As powder drying is by far the facility's most energy intensive process, this project therefore focuses exclusively on...

Empirical Evaluation of the Performance of CEVAE under Misspecification of the Latent Dimensionality

Causal machine learning deals with the inference of causal relationships between variables in observational datasets. <br/>For certain datasets, it is correct to assume a causal graph where information about unobserved confounders can only be obtained through noisy proxies, and CEVAE aims to address this case. <br/>The number of dimensions of...

Estimatic

Amsterdam Airport Schiphol has 5 runways, each of which can be used for take-off or landing of aeroplanes. The weather heavily influences which runway configuration air traffic control might pick. Airport Forecasting Service (AFOS) predicts which configuration of runways works most efficiently given a set of expected weather conditions and the...

Trace metal pollution in the Scheldt estuary - a statistical approach to estimate the metal partitioning coefficient for a suite of metals

Trace metals appear in estuarine systems in two forms: dissolved and particulate. <br/>Describing the partitioning between these two forms is done by a coefficient, K_d, which relies on a number of environmental parameters, such as the salinity of the water and the seasonally dependent biological activity. Although this coefficient is known to...