Monte Carlo Methods Based on Series Expansions for Ising-Type Models

We propose Monte Carlo methods to estimate the partition function of the Ising model.

The methods are based on the high-temperature series expansion of the partition function from statistical physics.

For the Ising model, typical Monte Carlo methods work well at high temperature, but fail in the low-temperature regime.

We demonstrate that the proposed Monte Carlo methods work differently: they behave particularly well at low temperature.

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About Mehdi Molkaraie

Mehdi Molkaraie received his BS from University of Tehran and his PhD from EPFL Switzelrand.

Since then he has been with the Department of Statistics and Actuarial Science University of

Waterloo, ETH-Zurich, and UPF-Barcelona. His research interests are in: graphical models, information theory, and Monte Carlo methods.