Estimation of Network Structures from Partially Observed Markov Random Fields
نویسنده
چکیده
We consider the estimation of high-dimensional network structures from partially observed Markov random field data using a `-penalized pseudo-likelihood approach. We fit a misspecified model obtained by ignoring the missing data problem. We derive an estimation error bound that highlights the effect of the misspecification. We report some simulation results that illustrate the theoretical findings.
منابع مشابه
Estimation of High-dimensional Partially-observed Discrete Markov Random Fields
We consider the problem of estimating the parameters of discrete Markov random fields from partially observed data in a high-dimensional setting. Using a `-penalized pseudo-likelihood approach, we fit a misspecified model obtained by ignoring the missing data problem. We derive an estimation error bound that highlights the effect of the misspecification. We report some simulation results that i...
متن کاملEvidence Estimation for Bayesian Partially Observed MRFs
Bayesian estimation in Markov random fields is very hard due to the intractability of the partition function. The introduction of hidden units makes the situation even worse due to the presence of potentially very many modes in the posterior distribution. For the first time we propose a comprehensive procedure to address one of the Bayesian estimation problems, approximating the evidence of par...
متن کاملAsymptotic Properties of Maximum (composite) Likelihood Estimators for Partially Ordered Markov Models
Partially ordered Markov models (POMMs) are Markov random fields (MRFs) with neighborhood structures derivable from an associated partially ordered set. The most attractive feature of POMMs is that their joint distributions can be written in closed and product form. Therefore, simulation and maximum likelihood estimation for the models is quite straightforward, which is not the case in general ...
متن کاملMultimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, we present a multimodal approach to the problem of mo...
متن کاملHierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorith...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011