Inference in Ising Models

Large pseudocounts and L[subscript 2]-norm penalties are necessary for the mean-field inference of Ising and Potts models

Learning Planar Ising Models

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this pa...

Supporting Information Appendix for Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods

1. Inference of couplings and of their accuracy within the inverse Ising model (page 2) 2. Limitations of the Ising model: higher-order correlations and couplings (page 7) 3. Algorithm for the Inverse Integrate-and-Fire Problem (page 11) 4. On Cross-correlograms: Analysis of Data, Relationship with Couplings, and Simulations (page 14) 5. Correspondence between Integrate-and-Fire and Ising Inver...

Dynamic Planar-Cuts: Efficient Computation of Min-Marginals for Outer-Planar Models

This paper deals with Dynamic MAP Inference, where the goal is to solve an instance of the MAP problem given that we have already solved a related instance of the problem. We propose an algorithm for Dynamic MAP Inference in planar Ising models, called Dynamic Planar-Cuts. As an application of our proposed approach, we show that we can extend the MAP inference algorithm of Schraudolph and Kamen...

بسط دمای بالای پذیرفتاری مدل آیزینگ شبکه کاگومه با برهم‌کنش نزدیکترین همسایه‌ها

 The Ising model is one of the simplest models describing the interacting particles. In this work, we calculate the high temperature series expansions of zero field susceptibility of ising model with ferromagnetic, antiferromagnetic and one antiferromagnetic interactions on two dimensional kagome lattice. Using the Pade´ approximation, we calculate the susceptibility of critical exponent of fer...

An Empirical Evaluation of Algorithms for Computing Equilibria in Games for Approximate Inference in Large Dimensional Probabilistic Graphical Models

Work in graphical models for game theory typically borrows from results in probabilistic graphical models. In this work, we instead consider the opposite direction. By using recent advances in equilibrium computation, we propose game-theoretic inspired, practical methods to perform probabilistic inference. We perform synthetic experiments using several different classes of Ising models, in orde...