Cluster variation method in statistical physics and probabilistic graphical models

Cluster Variation Method in Statistical Physics and Probabilistic Graphical Models

The cluster variation method (CVM) is a hierarchy of approximate variational techniques for discrete (Ising–like) models in equilibrium statistical mechanics, improving on the mean–field approximation and the Bethe–Peierls approximation, which can be regarded as the lowest level of the CVM. In recent years it has been applied both in statistical physics and to inference and optimization problem...

Statistical inference with probabilistic graphical models

These are notes from the lecture of Devavrat Shah given at the autumn school “Statistical Physics, Optimization, Inference, and Message-Passing Algorithms”, that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013. The school was organized by Florent Krzakala from UPMC & ENS Paris, Federico Ricci-Tersenghi from La Sapienza Roma, Lenka Zdeborová fro...

: metadiscourse in introduction sections of applied linguistics and physics research articles: exploring variation in frequency and type

abstract in written mode of language, metadiscourse markers are used commonly to help writers in general and academic writers in particular to produce coherent and professional texts. the purpose of the present study was to compare introduction sections of applied linguistics and physics articles regarding their use of interactive and interactional metadiscourse markers based on the model pro...

A Stochastic approximation method for inference in probabilistic graphical models

We describe a new algorithmic framework for inference in probabilistic models, and apply it to inference for latent Dirichlet allocation (LDA). Our framework adopts the methodology of variational inference, but unlike existing variational methods such as mean field and expectation propagation it is not restricted to tractable classes of approximating distributions. Our approach can also be view...

Probabilistic Graphical Models