Bayes factors and the geometry of discrete hierarchical loglinear models

A conjugate prior for discrete hierarchical loglinear models

In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the log-linear parameters or the cell probabilities parameters is a major challenge. In this paper, we define a flexible family of conjugate priors for the wide class of discrete hierarchical log-linear models, which includes the class of graphical models. These priors are defined as the Diaco...

Minimum Φ-divergence Estimator and Hierarchical Testing in Loglinear Models

In this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum φ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a φ-divergence goodness-of-fit statistic, which is the basis of two new statistics for solving the prob...

Underlying Distributions in Loglinear Models of Discrete Data

the study of bright and surface discrete cavity solitons dynamics in saturable nonlinear media

امروزه سالیتون ها بعنوان امواج جایگزیده ای که تحت شرایط خاص بدون تغییر شکل در محیط منتشر می-شوند، زمینه مطالعات گسترده ای در حوزه اپتیک غیرخطی هستند. در این راستا توجه به پدیده پراش گسسته، که بعنوان عامل پهن شدگی باریکه نوری در آرایه ای از موجبرهای جفت شده، ظاهر می گردد، ضروری است، زیرا سالیتون های گسسته از خنثی شدن پراش گسسته در این سیستم ها بوسیله عوامل غیرخطی بوجود می آیند. گسستگی سیستم عامل...

Teaching Inference Using Bayes and Discrete Models

Teaching classical statistical inference at an elementary level can be hard, due to the diiculty in teaching sampling distributions and the correct interpretation of statistical conndence. Bayesian methods have the attractive feature that statistical conclusions can be stated using the language of subjective probability. Simple methods of teaching Bayes rule are described and these methods are ...