Authors

• Farzad Eskandari ¹
• Sima Naghizade ²

¹ Assistant Professor, Allameh Tabatabaie University, Tehran, Iran

² Ph.D. Student, Tarbiat Modarres University, Tehran, Iran

Abstract

The purpose of this paper is to introduce the Baysian generalized dynamic models of non-parametric data.
   Following a Generalized Dynamic Bayesian statistical inference Paradigm, in this paper, we provide a new approach for multiparameters logistic regression when we have a nonlinear model between  and . In this approach Response Variable  and the coefficients of the model  are depended of time t.
    The proposed procedure is illustrated by applying a data set for unemployment in Iran, which has previously been analyzed by various authors. 

Keywords

• Dynamic Model
• Mutiparameters Exponential Distributions
• Generalized Inverse
• Kalman Filter