Prior specification of neighbourhood and interaction structure in binary Markov random fields

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Fully Bayesian binary Markov random field models: Prior specification and posterior simulation

We propose a flexible prior model for the parameters of a binary Markov random field (MRF) defined on a rectangular lattice and with k ×l cliques. The prior model allows higher-order interactions to be included in the MRF. We also define a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to sample from the associated posterior distribution. The number of possible parameters for an MR...

متن کامل

Structure Learning in Markov Random Fields

Scoring structures of undirected graphical models by means of evaluating the marginal likelihood is very hard. The main reason is the presence of the partition function which is intractable to evaluate, let alone integrate over. We propose to approximate the marginal likelihood by employing two levels of approximation: we assume normality of the posterior (the Laplace approximation) and approxi...

متن کامل

Greedy Structure Learning of Markov Random Fields

Acknowledgments I would like to thank my advisor, Pradeep Ravikumar, for inspiration, guidance, and encouragement on this work. In addition, I would like to thank Ali Jalali for his collaboration and work on the proof techniques and theoretical analysis used in this paper. Also, I would also like to thank Inderjit Dhillon and the students of his lab for motivation and many stimulating conversat...

متن کامل

Binary pattern recognition using Markov random fields and HMMs

In this paper we present a stochastic framework for the recognition of binary random patterns which advantageously combine hmms and Markov random elds (mrfs). The hmm component of the model analyzes the image along one direction, in a speci c state observation probability given by the product of causal mrf-like pixel conditional probabilities. Aspects concerning de nition, training and recognit...

متن کامل

Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior

In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive crossvalidation to find the optimal regularization parameter. Moreover, the model’s predict...

متن کامل

ذخیره 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: Statistics and Computing

سال: 2016

ISSN: 0960-3174,1573-1375

DOI: 10.1007/s11222-016-9650-5