Gaussian Markov Random Fields: Theory and Applications
نویسنده
چکیده
منابع مشابه
Graph-based LearningModels for Information Retrieval: A Survey
3 Graph Analysis 6 3.1 Analysis Based on Spectral Graph Theory . . . . . . . . . . . . . 7 3.2 Analysis Based on Random Field Theory . . . . . . . . . . . . . . 9 3.2.1 Markov Random Fields . . . . . . . . . . . . . . . . . . . . 9 3.2.2 Conditional Random Fields . . . . . . . . . . . . . . . . . 10 3.2.3 Gaussian Random Fields . . . . . . . . . . . . . . . . . . . 11 3.3 Analysis Based onMatri...
متن کاملThink continuous: Markovian Gaussian models in spatial statistics
Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spatial statistics. Unfortunately, it has traditionally been difficult to link GMRFs with the more traditional Gaussian random field models as the Markov property is difficult to deploy in continuous space. Following the pioneering work of Lindgren et al. (2011), we expound on the link between Marko...
متن کاملA Different Construction of Gaussian Fields from Markov Chains: Dirichlet Covariances Une Nouvelle Construction De Champs Gaussiens À Partir De Chaînes De Markov
– We study a class of Gaussian random fields with negative correlations. These fields are easy to simulate. They are defined in a natural way from a Markov chain that has the index space of the Gaussian field as its state space. In parallel with Dynkin’s investigation of Gaussian fields having covariance given by the Green’s function of a Markov process, we develop connections between the occup...
متن کاملIMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...
متن کاملAdaptive Gaussian Markov Random Fields with Applications in Human Brain Mapping
Functional magnetic resonance imaging (fMRI) has become the standard technology in human brain mapping. Analyses of the massive spatio–temporal fMRI data sets often focus on parametric or nonparametric modeling of the temporal component, while spatial smoothing is based on Gaussian kernels or random fields. A weakness of Gaussian spatial smoothing is underestimation of activation peaks or blurr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Technometrics
دوره 48 شماره
صفحات -
تاریخ انتشار 2006