Bayesian statistical modelling and data analysis
I build statistical models that turn incomplete, noisy data into estimates and predictions with honest uncertainty. Typical questions: how a drug's concentration evolves across a patient population and how much individuals differ; which readings in a sensor network are implausible and why; how regions compare once small sample sizes are taken into account. Results are delivered as reproducible analyses, reports and interactive dashboards, as open-source software.
How do drug concentrations and a biomarker respond to a dose, how much do patients differ, and which daily dose keeps most patients in range? A population model of the classic warfarin data: plasma concentrations and clotting activity in 32 people after one dose.
Data: O'Reilly & Aggeler (1963, 1968), as distributed in the nlmixr2data R package.
How fast is the share of young children receiving integration assistance changing in each district? Six yearly values give each district a noisy trend; a Fay–Herriot-type small-area model pools each district towards its state as far as its data warrant.
Data: © Statistische Ämter des Bundes und der Länder, Deutschland, 2026 (Regionalstatistik 22161-01-01-4, 12411-04-02-4; reference dates 2020–2025), dl-de/by-2-0. Kreis boundaries: © GeoBasis-DE / BKG (2026), dl-de/by-2-0.
How fast is Germany warming, and how sure can we be? A smooth trend fitted to annual means since 1881 and continued ten years ahead with its uncertainty.
Data: Deutscher Wetterdienst (DWD), annual mean air temperature for Germany, modified (CC BY 4.0).
Has every part of Germany warmed alike? Each state's smoothed temperature trend against its own 1961–1990 level, as an animated map from 1881 to today.
Data: Deutscher Wetterdienst (DWD), annual mean air temperature by state, modified (CC BY 4.0). State boundaries: © GeoBasis-DE / BKG (2026), dl-de/by-2-0.
Which readings of Germany's ambient gamma dose rate network need a second look? A Bayesian model of each probe (baseline, daily cycle, rain washout, rare unexplained readings) separates rain peaks from readings it cannot explain.
Data: Bundesamt für Strahlenschutz (BfS), ODL-Info open data, hourly dose rate and radar precipitation, 14–20 October 2026; model results as of 20 October 2026.
A fixed threshold raises a pre-alarm whenever rain lifts the reading. Across the whole network, the model explains rain peaks and keeps only the pre-alarms it cannot explain.
Data: Bundesamt für Strahlenschutz (BfS), ODL-Info open data, 14–20 October 2026; model results as of 20 October 2026. State boundaries: © GeoBasis-DE / BKG (2026), dl-de/by-2-0.
Did a programme change the outcome, and when? Municipalities adopt it in different years; an event-study model compares adopters with non-adopters year by year.
Synthetic data: 60 simulated municipalities over 12 years.
Regression models written as brms-style formulas, compiled to Stan.
Interactive web charts from AlgebraOfGraphics plot specifications. The charts on this page are made with it.
Write and compose Stan models in Julia: models compile to Stan and can be sampled or optimised directly.
Describe a computation as a graph, say what is known and what is wanted, and get a specialised kernel that computes only what is needed.
Adaptive warmup for Hamiltonian Monte Carlo, including automatic reparametrization of posteriors that are hard to sample.
Nested, thread-safe progress bars organised as a tree.
Interactive web dashboards built from plain data objects, with static export. This site is one.
Objects with lazily computed, optionally disk-cached properties: the dependency graph behind the analyses and apps.
Estimators for nonlinear mixed-effects models that work with any model providing a small set of functions.
A fast, allocation-free implementation of the No-U-Turn sampler.
Information pursuant to § 5 DDG
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