Central to the success of adaptive systems is their ability to interpret signals from their
environment and respond accordingly—they act as agents interacting with their …

Many inference scenarios rely on extracting relevant information from known data in order to
make future predictions. When the underlying stochastic process satisfies certain …

Stochastic modeling is an essential component of the quantitative sciences, with hidden
Markov models (HMMs) often playing a central role. Concurrently, the rise of quantum …

We apply techniques from the field of computational mechanics to evaluate the statistical
complexity of neural recording data from fruit flies. First, we connect statistical complexity to …

Stochastic processes abound in nature and accurately modeling them is essential across
the quantitative sciences. They can be described by hidden Markov models (HMMs) or by …

Quantum technologies offer a promising route to the efficient sampling and analysis of
stochastic processes, with potential applications across the sciences. Such quantum …

How can we learn about the nature of relationships that people form online? We argue that
topic models can produce content-based representations of social relationships that are …

Modelling systems in nature as predictable Markov processes is efficient, however many
systems are often embedded in a larger and more complex environment, or have many …

Predicting a stochastic process' future lies at the heart of many scientific areas. A predictive
model extracts information from a stochastic process' past and uses it to generate future …