|
|
|
|
|
 |
Search published articles |
 |
|
Showing 3 results for Shape Analysis
Mahnaz Nabil, Mousa Golalizadeh,
Volume 8, Issue 2 (3-2015)
Abstract
Recently, employing multivariate statistical techniques for data, that are geometrically random, made more attention by the researchers from applied disciplines. Shape statistics, as a new branch of stochastic geometry, constitute batch of such data. However, due to non-Euclidean feature of such data, adopting usual tools from the multivariate statistics to proper statistical analysis of them is not somewhat clear. How to cluster the shape data is studied in this paper and then its performance is compared with the traditional view of multivariate statistics to this subject via applying these methods to analysis the distal femur.
S. Morteza Najibi, Mousa Golalizadeh, Mohammad Reza Faghihi,
Volume 9, Issue 2 (2-2016)
Abstract
In this paper, we study the applicability of probabilistic solutions for the alignment of tertiary structure of proteins and discuss its difference with the deterministic algorithms. For this purpose, we introduce two Bayesian models and address a solution to add amino acid sequence and type (primary structure) to protein alignment. Furthermore, we will study the parameter estimation with Markov Chain Monte Carlo sampling from the posterior distribution. Finally, in order to see the effectiveness of these methods in the protein alignment, we have compared the parameter estimations in a real data set.
Meysam Moghimbeygi, Mousa Golalizadeh,
Volume 13, Issue 1 (9-2019)
Abstract
Recalling the definition of shape as a point on hyper-sphere, proposed by Kendall, the regression model is studied in this paper. In order to simplify the modeling, the triangulation via two landmarks is proposed. The triangulation not only simplifies the regression modelling of the shapes but also provides straightforward computation procedure to reconstruct geometrical structure of the objects. Novelty of the proposed method in this paper is on using the predictor variable, based upon the shape, which suitably describes the geometrical variability of the response. The comparison and evaluation of the proposed methods with the full Procrustes matching through the mean square error criteria are done. Application of two models for the configurations of rat skulls is investigated.
|
|
|
|
|
|
|
|
|
|
|
