Humboldt-Universität zu Berlin - Emmy Noether Research Group


Topics for Theses


  • Using posterior predictive checks with semistructured deep distributional regression
  • Efficient estimation of distributional regression models using HMC based on normalizing flows
  • Predicting revenue streams of small and medium-sized enterprises
  • Understanding and implementing predictive information criteria for Bayesian models
  • Clustering with Non-Uniform Random
  • Forests Testing for Rational Bubbles in Financial Markets
  • Counterfactual Analysis
  • Asymptotic behaviour of the posterior in overfitted (deep) mixture models
  • Posterior concentration rates for Bayesian high-dimensional sparse additive models
  • Deep distributional learning
  • Standard errors of Variational Inference
  • Uncertainty quantification for deep learning
  • Bayesian stacking approaches
  • Bayesian neural network structures
  • Deep Gaussian process modelling
  • Multiple Imputation for Panel Data
  • Using Stacking to Average Distributional Regression Models
  • Modelling Income Inequality with Semiparametric Transformation Models
  • Bayesian Nonparametric Conditional Density Estimators
  • Distributional Joint Modelling
  • Approximations of Normalizing Constants in Doubly-Intractable Likelihoods
  • Effect Fusion of Categorial Predictors
  • Effect Selection in Semiparametric Quantile Regression Models
  • Measuring the Explained Variance in Structured Additive Distributional Regression
  • Comparisons and Implementation of Non-Local Shrinkage Priors
  • Predictive information criteria from a Bayesian perspective


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Completed Theses


Master Theses

  • Felix Germaine (2022): Interpretable modelling of ICU patients remaining length-of-stay distribution using tabular patient data, clinical notes and irregularly spaced clinical measurements
  • Jana Kleinemeier (2021): Using Variational Inference to Estimate Structred Additive Distributional Regression Models
  • Yuliya Vandysheva (2021): Die Modellierung der COVID-19 Fallzahlen in Abhängigkeit von Strukturdaten zu Wetter und Bevölkerung in Berlin
  • Jost von Petersdorff-Campen (2021): Copula Regression for Discrete Data - An Application to Covid-19
  • Chris Kolb (2021): Application of Deep Distributional Regression
  • Per Joachims (2021): Uncertainty Quantification with Bayesian Neural Networks
  • Clara Hoffmann (2021): Marginally Calibrated Posterior Densities for End-to-End Learning in Autonomous Driving
  • Silvia Ventoruzzo (2021): On the Role of Weather in Predicting Online Sales of Different Product Categories
  • Saifoul Alom (2021): E-Mail-Klassiffzierung unten Anwendung von Machine-Learning zur Optimierung des Kundendienstes
  • Michael Schimpke (2021): Sentiment-Optimised Translation: An Approach for Multilingual Sealing of Pre-Trained Sentiment Classiffers
  • Jakub Kondek (2021): Classiffcation of Empathic and Non-Empathic Emails using Prototypical Networks
  • Lukas Moedl (2021): The RODEO Approach for Nonparametric Density Estimation
  • Anna Elisabeth Riha (2021): Hyperpriorsensitivity of Bayesian Wrapped Gaussian Processes with an Application to Wind Data
  • Jil Kollmus-Heege (2020): Dynamic Prediction in Flexible Bayesian Additive Joint Models
  • Kang Yang (2020): Deep Generalised Additive Regressive Model for Location, Scale and Shape using Tensorflow
  • Paulina Kurowska (2020): Bayesian Variable Selection Approach in Additive Quantile Regression
  • Patricia Craja (2019): Fraud Detection in Financial Statements using Deep Learning based Natural Language Processing
  • Yinan Wu (2019): Bayesian Adaptive Lasso for Zero Inflated Count Model and Overdispersion
  • Jerome Bau (2019): Context-aware Sentence Generation from Keywords
  • Lukas Kemkes (2019): Comparison and combination of extractive and abstractive techniques for machine text summarization
  • Max Reinhardt (2019): Uncertainty in Deep Neural Networks
  • Jan Reher (2019): Purchase Prediction with Topic Models Using Variational Inference
  • Andrii Zakharov (2019): Development of a Customer Insolvency Prediction Model following the Cross-Industry Standard Process for Data Mining (CRISP-DM)
  • Stanislaus Stadlmann (2017): bamlss.vis: An R Package to Interactively Analyze and Visualize Bayesian Additive Models for Location, Scale and Shape (BAMLSS) Using the Shiny Framework
  • Hannes Riebl (2017): Multivariate Quantile Residuals in Copula Regression: Model Evaluation and Selection
  • Manuel Carlan (2016): Bayesian Variable Selection in Semiparametric Regression
  • Paul Wiemann (2015): Statistische Analyse von Advice-Taking
  • Manuel Vonrueti (2015): Hyperparameterwahl einer neuartigen Spike und Slab Priori für Verteilungsregression
  • Anastasia Gorbunova (2015): Statistische Analyse von länderspezifischen Energiemixen

Bachelor Theses

Julius Freidank (2021): Vergleich von Vorhersagemodellen zu Stornierungen von Hotelbuchungen

Yuliya Vandysheva (2021): Die Modellierung der COVID-19 Fallzahlen in Abhängigkeit von Strukturdaten zu Wetter und Bevölkerung in Berlin

Elisabeth Krawczyk (2019): Comparing applicability of prevalent Clustering Algorithms for Document Clustering

Hannes Riebl (2014): Power Law Verteilungen in Generalisierten Additiven Modellen für Lokation, Skala und Form

Philipp Hanheide (2013): Messung der Armuts-Vulnerabilität