The present study aims at exploring predictors influencing mathematics performance. In particular, the research focuses on four subject components such as motivation, attitude towards mathematics, learning style, and teaching strategies.

# Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation. (arXiv:2105.05901v1 [stat.ME])

Background: The Expected Value of Sample Information (EVSI) calculates the value of collecting additional information through a study with a given design. Standard EVSI analyses assume that the treatment recommendations

# A new characterization of discrete decomposable models. (arXiv:2105.05907v1 [math.ST])

Decomposable graphical models, also known as perfect DAG models, play a fundamental role in standard approaches to probabilistic inference via graph representations in modern machine learning and statistics. However, such

# A new perspective on low-rank optimization. (arXiv:2105.05947v1 [math.OC])

A key question in many low-rank problems throughout optimization, machine learning, and statistics is to characterize the convex hulls of simple low-rank sets and judiciously apply these convex hulls to

# Two-step method for assessing dissimilarity of random sets. (arXiv:2105.05952v1 [stat.ME])

The paper concerns a new statistical method for assessing dissimilarity of two random sets based on one realisation of each of them. The method focuses on shapes of the components

# Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models. (arXiv:2105.05953v1 [stat.ML])

Mixed linear regression (MLR) model is among the most exemplary statistical tools for modeling non-linear distributions using a mixture of linear models. When the additive noise in MLR model is

# Characterizing Logarithmic Bregman Functions. (arXiv:2105.05963v1 [math.ST])

Minimum divergence procedures based on the density power divergence and the logarithmic density power divergence have been extremely popular and successful in generating inference procedures which combine a high degree

# SPUX Framework: a Scalable Package for Bayesian Uncertainty Quantification and Propagation. (arXiv:2105.05969v1 [stat.CO])

We present SPUX – a modular framework for Bayesian inference enabling uncertainty quantification and propagation in linear and nonlinear, deterministic and stochastic models, and supporting Bayesian model selection. SPUX can

# Optimal transport with some directed distances. (arXiv:2105.05989v1 [cs.IT])

We present a toolkit of directed distances between quantile functions. By employing this, we solve some new optimal transport (OT) problems which e.g. considerably flexibilize some prominent OTs expressed through

# Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective. (arXiv:2105.06018v1 [cs.LG])

This paper is concerned with multi-modal data fusion (MMDF) under unexpected modality failures in nonlinear non-Gaussian dynamic processes. An efficient framework to tackle this problem is proposed. In particular, a