Algebraic Statistics is concerned with the interplay of techniques from commutative algebra, combinatorics, (real) algebraic geometry, and related fields with problems arising in statistics and data science. This workshop ...

Technological developments have created a constant incoming stream of complex new data structures that need analysis. Modern statistics therefore means mathematically sophisticated new statistical theory that generates or ...

The aim of the highly successful workshop Computationally and statistically efficient inference for large-scale and heterogeneous data was to foster dissemination and collaboration between researchers in the area of ...

This was the fifth workshop on mathematical and statistical methods on the transmission of infectious diseases. Building on epidemiologic models which were the subject of earlier workshops, this workshop concentrated on ...

While causal inference is established in some disciplines such as econometrics and biostatistics, it is only starting to emerge as a valuable tool in areas such as machine learning and artificial intelligence. The ...

The goal of this workshop was to discuss recent developments of nonparametric statistical inference. A particular focus was on high dimensional statistics, semiparametrics, adaptation, nonparametric bayesian statistics, ...

Learning theory studies data structures from samples and aims at understanding unknown function relations behind them. This leads to interesting theoretical problems which can be often attacked with methods from Approximation ...

The nomenclature “data assimilation” arises from applications in the geosciences where complex mathematical models are interfaced with observational data in order to improve model forecasts. Mathematically, data assimilation ...