Search Results for author: Till Hoffmann

Found 7 papers, 5 papers with code

Minimising the Expected Posterior Entropy Yields Optimal Summary Statistics

1 code implementation6 Jun 2022 Till Hoffmann, Jukka-Pekka Onnela

Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference.

Bayesian Inference Dimensionality Reduction +1

Wastewater catchment areas in Great Britain

1 code implementation ESSOAr 2022 Till Hoffmann, Sarah Bunney, Barbara Kasprzyk-Hordern, Andrew Singer

Wastewater catchment area data are essential for wastewater treatment capacity planning and have recently become critical for operationalising wastewater-based epidemiology (WBE) for COVID-19.

Epidemiology

Thematic recommendations on knowledge graphs using multilayer networks

no code implementations12 May 2021 Mariano Beguerisse-Díaz, Dimitrios Korkinof, Till Hoffmann

We also apply our approach to movie recommendation using publicly-available data to ensure the reproducibility of our results.

Collaborative Filtering Knowledge Graphs +1

Cost-based feature selection for network model choice

no code implementations19 Jan 2021 Louis Raynal, Till Hoffmann, Jukka-Pekka Onnela

This approach reduced the computational cost by a factor of 50 without affecting classification accuracy.

feature selection Model Selection

Inference of a universal social scale and segregation measures using social connectivity kernels

1 code implementation12 Aug 2020 Till Hoffmann, Nick S. Jones

How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives.

Social and Information Networks Physics and Society Methodology

Community detection in networks without observing edges

1 code implementation18 Aug 2018 Till Hoffmann, Leto Peel, Renaud Lambiotte, Nick S. Jones

We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes.

Community Detection

Unified treatment of the asymptotics of asymmetric kernel density estimators

1 code implementation10 Dec 2015 Till Hoffmann, Nick S. Jones

We extend balloon and sample-smoothing estimators, two types of variable-bandwidth kernel density estimators, by a shift parameter and derive their asymptotic properties.

Methodology Statistics Theory Statistics Theory

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