Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

Classical causal and statistical inference methods typically assume the observed data
consists of independent realizations. However, in many applications this assumption is …

Background Compositional systems, represented as parts of some whole, are ubiquitous.
They encompass the abundances of proteins in a cell, the distribution of organisms in …

Missing records are a perennial problem in analysis of complex data of all types, when the
target of inference is some function of the full data law. In simple cases, where data is …

" Today's science is characterised by an ever-increasing amount of data, due to instru-
mental and experimental progress in monitoring and manipulating complex systems made …

A fundamental problem associated with the task of network reconstruction from dynamical or
behavioral data consists in determining the most appropriate model complexity in a manner …

Using methods of statistical physics, we analyse the error of learning couplings in large Ising
models from independent data (the inverse Ising problem). We concentrate on learning …

Factor models have been proposed for a broad range of observed variables such as binary,
Gaussian, and variables of mixed types. They typically model a pairwise interaction …

The inference performance of the pseudolikelihood method is discussed in the framework of
the inverse Ising problem when the ℓ 2-regularized (ridge) linear regression is adopted. This …

Pairwise models like the Ising model or the generalized Potts model have found many
successful applications in fields like physics, biology, and economics. Closely connected is …