

A powerful differential equation for Ising-decorated maps in arbitrary genus
De Ariane Carrance


Wasserstein gradient flows and applications to sampling in machine learning - lecture 1
De Anna Korba
Apparaît dans la collection : 2016 - T1 - WS4 - Inference problems theme
We consider the problem of estimating the partition function of the ferromagnetic q-state Potts model. We propose an importance sampling algorithm in the dual of the normal factor graph representing the model. The algorithm can efficiently compute an estimate of the partition function when the coupling parameters of the model are strong (corresponding to models at low temperature) or when the model contains a mixture of strong and weak couplings. We show that, in this setting, the proposed algorithm significantly outperforms the state-of-the-art methods.