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

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

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.

Continuing from Bayesian experimental design, we now consider adaptivity.

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.

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.

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.

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.

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

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.