In this paper, we propose an unsupervised color image segmentation scheme using homotopy continuation method and Compound Markov Random Field (CMRF) model. The proposed scheme is recursive in nature where model parameter estimation and the image label estimation are alternated. Ohta (I1, I2, I3) model is used as the color model for image segmentation and we propose a compound MRF model taking c...

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models and it is very common to encounter missing data. ...

A three step method for obtaining nearly maximum likelihood ARMA spectral estimates is presented. The computational complexity of the algorithm is comparable to YuleWalker methods, but the method gives asymptotically efficient estimates. The implementation of the algorithm is discussed, and numerical examples are presented to illustrate its performance.

In this paper, we introducte a family of univariate Birnbaum-Saunders distributions arising from the skew-normal-t  distribution. We obtain several properties of this distribution such as its moments, the maximum likelihood estimation procedure via an EM-algorithm and a method to evaluate standard errors using the EM-algorithm. Finally, we apply these methods to a real data set to demonstr...

Data on 368 birth records of Swedish Red and White cattle herd raised organically at Dogan Organic Dairy Cattle Farm for a period of 2006 and 2009 (4 years) were used to estimate the effects of different environmental factors on birth weight. Additionally, a comparison of four methods for variance component estimation to compute heritability of birth weight was performed using ML (Maximum likel...

Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods...

the aim of the present study was to estimate the genetic parameters and genetic trends of somatic cell score (scs) in the first 3 lactations of iranian holstein cows by random regression (rr) animal model by using restricted maximum likelihood (reml) method. the data set used in this analysis included observations of 340318 test-day records of 41526 cows in 288 herds; 89969 test-day of 11750 co...

The present study was carried out to estimate (co)variance components and genetic parameters for some productivity traits of Baluchi ewes. The data were collected during a 31 year period (1984-2014) at the experimental breeding station of Baluchi sheep, which is located in north-east of Mashhad, Iran. The analysis was based on 14030 records of lambs and 4371 records of ewes. Investigated traits...

We propose a new method for parameter learning in Bayesian networks with qualitative influences. This method extends our previous work from networks of binary variables to networks of discrete variables with ordered values. The specified qualitative influences correspond to certain order restrictions on the parameters in the network. These parameters may therefore be estimated using constrained...