
Exact linear regression
Exact methods supersede approximations used in high-dimensional linear regression to find correlations in statistical physics problems.
The London Institute’s papers are the official record of our discoveries. They allow others to build on and apply our work. Each one is the result of many months of research, so we strive to make them clear, inspiring and beautiful, and publish them in leading journals.

Exact methods supersede approximations used in high-dimensional linear regression to find correlations in statistical physics problems.

Statistical methods that normally fail for very high-dimensional data can be rescued via mathematical tools from statistical physics.

The generation of large graphs with a controllable number of short loops paves the way for building more realistic random networks.

We optimize Bayesian data clustering by mapping the problem to the statistical physics of a gas and calculating the lowest entropy state.
An explicit analytical solution reproduces the main features of random graph ensembles with many short cycles under strict degree constraints.
Properties of protein interaction networks test the reliability of data and hint at the underlying mechanism with which proteins recruit each other.
Exact equations for the thermodynamic quantities of lattices made of d-dimensional hypercubes are obtainable with the Bethe-Peierls approach.
The analysis of real networks which contain many short loops requires novel methods, because they break the assumptions of tree-like models.
Explicit formulae for the Shannon entropies of random graph ensembles provide measures to compare and reproduce their topological features.
The immune system must simultaneously recall multiple defense strategies because many antigens can attack the host at the same time.
Associative networks with different loads model the ability of the immune system to respond simultaneously to multiple distinct antigen invasions.
Unbiased randomisation processes generate sophisticated synthetic networks for modelling and testing the properties of real-world networks.
Methods from tailored random graph theory reveal the relation between true biological networks and the often-biased samples taken from them.
A transfer operator formalism solves the macroscopic dynamics of disordered Ising chain systems which are relevant for ageing phenomena.
New mathematical tools quantify the topological structure of large directed networks which describe how genes interact within a cell.