Transmission matrix inference via pseudolikelihood decimation
نویسندگان
چکیده
Abstract Recently, significant efforts in medical imaging are towards the exploitation of disordered media as optics tools. Among several approaches, transmission matrix description is promising for characterizing complex structures and, currently, has enabled and focusing through disorder. In present work, we report a statistical mechanics problem. We convert linear input–output recovery into inference an effective interaction matrix. do this by relying on pseudolikelihood maximization process based random intensity observations. Our aim to bridge results from spin-glass theory field photonics, uncovering insights scattering problem encouraging development novel techniques better investigations.
منابع مشابه
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-ofsample correlations than L2 regularized maximum pseudolikelihood inference as well as mean field and isolated spin pair approximations with pseudocount regularization. The key to the approach is a variation...
متن کاملPseudolikelihood decimation algorithm improving the inference of the interaction network in a general class of Ising models.
In this Letter we propose a new method to infer the topology of the interaction network in pairwise models with Ising variables. By using the pseudolikelihood method (PLM) at high temperature, it is generally possible to distinguish between zero and nonzero couplings because a clear gap separate the two groups. However at lower temperatures the PLM is much less effective and the result depends ...
متن کاملOn pseudolikelihood inference for semiparametric models with boundary problems
Consider a semiparametric model indexed by a Euclidean parameter of interest and an infinite-dimensional nuisance parameter. In many applications, pseudolikelihood provides a convenient way to infer the parameter of interest, where the nuisance parameter is replaced by a consistent estimator. The purpose of this paper is to establish the asymptotic behaviour of the pseudolikelihood ratio statis...
متن کاملPenalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates
Given spatially located observed random variables (x; z) = f(x i ; z i)g i , we propose a new method for nonparametric estimation of the potential functions of a Markov Random Field p(xjz), based on a roughness penalty approach. The new estimator maximises the penalized log-pseudolikelihood function and is a natural cubic spline. The calculations involved do not rely on Monte Carlo simulation. ...
متن کاملExploiting Eigenvalues of the Hessian Matrix for Volume Decimation
In recent years the Hessian matrix and its eigenvalues became important in pattern recognition. Several algorithms based on the information they provide have been introduced. We recall the relationship between the eigenvalues of Hessian matrix and the 2nd order edge detection filter, show the usefulness of treating them separately and exploit these facts to design a combined threshold operation...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics A
سال: 2022
ISSN: ['1751-8113', '1751-8121']
DOI: https://doi.org/10.1088/1751-8121/ac8c06