Learning Mixtures of Ising Models using Pseudolikelihood
Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a mixture of Ising models. The performance of the proposed approach is demonstrated for Ising and Potts models on both synthetic and real data.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Variational Pseudolikelihood for Regularized Ising Inference
I propose a variational approach to maximum pseudolikelihood inference of the Ising model. The variational algorithm is more computationally efficient, and does a better job predicting out-of-sample correlations than $L_…
Local Perturb-and-MAP for Structured Prediction
Conditional random fields (CRFs) provide a powerful tool for structured prediction, but cast significant challenges in both the learning and inference steps. Approximation techniques are widely used in both steps, which …
Combinatorial Optimizationglobal-optimizationPredictionStructured PredictionInferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting
Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inhe…
Interaction Screening and Pseudolikelihood Approaches for Tensor Learning in Ising Models
In this paper, we study two well known methods of Ising structure learning, namely the pseudolikelihood approach and the interaction screening approach, in the context of tensor recovery in $k$-spin Ising models. We show…
Efficiently learning and sampling multimodal distributions with data-based initialization
We consider the problem of sampling a multimodal distribution with a Markov chain given a small number of samples from the stationary measure. Although mixing can be arbitrarily slow, we show that if the Markov chain has…