Strouwen Statistics

New Conference Paper: Experimental Design for Missing Physics

Tue, 30 Dec 2025 00:00:00 +0000

I’m excited to announce that Sebastian Micluța-Câmpeanu and I have published “Experimental Design for Missing Physics” at DYCOPS 2025.

MixedModelsSmallSample.jl: Small Sample Inference for Mixed Models in Julia

Fri, 18 Jul 2025 00:00:00 +0000

Announcing the registration of MixedModelsSmallSample.jl in the Julia General Registry!

Notes on policy equivalence in adaptive Bayesian experimental design

Mon, 01 Apr 2024 00:00:00 +0000

Continuing from Bayesian experimental design and adaptive Bayesian experimental design, we can make precise why the nested adaptive criterion is equivalent to an optimal policy problem.

Notes on adaptive Bayesian experimental design

Thu, 01 Feb 2024 00:00:00 +0000

Continuing from Bayesian experimental design, we now consider adaptivity.

Notes on Bayesian experimental design

Tue, 01 Aug 2023 00:00:00 +0000

For much of the experimental design literature, Bayesian experimental design is synonymous with the expected Kullback-Leibler divergence. It is not obvious at first glance why this criterion leads to informative experiments, so let’s dive a bit deeper into Bayesian experimental design.

New paper about Experimental Design using the Kalman Filter

Fri, 31 Mar 2023 00:00:00 +0000

I am happy to announce the publication of our paper “Adaptive and robust experimental design for linear dynamical models using Kalman filter” in Statistical Papers.

Current experimental design techniques for dynamical systems often only incorporate measurement noise, while dynamical systems also involve process noise.

Design for model discrimination using symbolic regression

Sun, 01 Jan 2023 00:00:00 +0000

Most experimental design focusses on parameter precision, where the model structure is assumed known and fixed. But arguably finding the correct model structure is the part of the modelling process that takes the most effort. In this blog we will look at automating this process using symbolic regression, and to do this with gathering too much data.

Notes on Probabilistic Programming for Stochastic Differential Equations

Mon, 01 Aug 2022 00:00:00 +0000

Some notes on Bayesian inference for stochastic differential equations in Julia. Specifically, inference for θ and σ of the Ornstein–Uhlenbeck process. The explanation is quite terse, since in the end, I was not unable to get this to work on larger problems.

Notes on adjoint sensitivity analysis of dynamic systems part 2

Sat, 30 Apr 2022 00:00:00 +0000

We continue from part 1 with a more rigorous version of the derivation of adjoint sensitivity analysis for continuous time systems,

Notes on adjoint sensitivity analysis of dynamic systems part 1

Mon, 31 Jan 2022 00:00:00 +0000

Gradients are useful for efficient parameter estimation and optimal control of dynamic systems. Calculating these gradients requires sensitivity analysis. Sensitivity analysis for dynamic systems comes in two flavors, forward mode and adjoint (reverse). For systems with a large number of parameters adjoint sensitivity analysis is often more efficient [1]. I find that the traditional way of deriving adjoints for ordinary differential equations, such as [3], leaves me with little intuition what these equations represent. The goal of this blog post is to gain some intuition about these equations by deriving the adjoint equations in a different way.

Dynamic experimental design in 100 lines of Julia code

Thu, 30 Sep 2021 00:00:00 +0000

Optimal experimental design is an area of statistics focused on constructing informative experiments. In this tutorial we introduce the necessary tools to construct such informative experiments for dynamic systems using only 100 lines of Julia code. We will work with a well-mixed fed-batch bioreactor as an example system. We have quite a bit of domain knowledge how to model the behavior of such a reactor. The reactor has three dynamic states: the substrate concentration \(C_s\), the biomass concentration \(C_x\) and the volume of the reactor \(V\). The evolution in time of these states is governed by the following differential equations: