bayesian inference

The instrumental variable (IV) regression is a common model in econometrics and other applied disciplines. This model is one of the proper candidate in dealing with endogeneity phenomenon which occurs in analyzing the multivariate regression when the errors are correlated with some covariates. One can consider IV regression as a special case of simultaneous equation models (SEM). There are some cases in which the normality assumption might not hold for the error term in these models and so the skew-normal distribution might be a suitable candidate. The present paper tackle the Bayesian inference based on Markov Chain Monte Carlo (MCMC) using this density for the error term while some instrumental variables are considered in the corresponding regression model. The proposed model is utilized to analysis the Iranian rural households income and expenditure collected in 2009.

The Exponentiated Gumbel (EG) distribution has been proposed to capture some aspects of the data that the Gumbel distribution fails to specify. In this paper, we estimate the EG's parameters in the Bayesian framework. We consider a 2-level hierarchical structure for prior distribution. As the posterior distributions do not admit a closed form, we do an approximated inference by using Gibbs and Metropolis-Hastings algorithm.

In many medical studies، in order to describe the course of illness and treatment effects، longitudinal studies are used. In longitudinal studies، responses are measured frequently over time، but sometimes these responses are discrete and with two-state. Recently Binary quantile regression methods to analyze this kind of data have been taken into consideration. In this paper، quantile regression model with Lasso and adaptive Lasso penalty for longitudinal data with dichotomous responses is provided. Since in both methods posteriori distributions of the parameters are not in explicit form، thus the full conditional posteriori distributions of parameters are calculated and the Gibbs sampling algorithm is used to deduction. To compare the performance of the proposed methods with the conventional methods، a simulation study was conducted and at the end، applications to a real data set are illustrated.

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.

There are several methods for inference about gene networks، but there are few cases in which the historical information have been considered. In this research we deal with Bayesian inference on gene network. We apply a Bayesian framework to use the available information. Assuming a proper prior distribution and taking the dependency of parameters into account، we seek a model to obtain promising results. We also deal with the hyper parameter estimation. Two methods are considered. The results will be compared by the use of a simulation based on Gibbs sampler. The strengths and weaknesses of each method are briefly mentioned.

In this article, we are going to interpret one of the queueing systems with a single server that has general and unknown distribution for the service and interarrival time using Bayesian view point.These General distributions may be well approximated by family of Mixed General Erlang distribution (MGE). This distribution is reparametrized such that it is possible to define a non-informative prior which allows using the MCMC method for estimating the parameters of models.Using the results of simulating parameters, we can estimate interesting measures that are based on recent results for known parameters in GI/G/1 frequently implemented queueing systems. At the end, we illustrate our approach for analyzing GI/G/1 systems with data from a bank.

Today, the predictive results of sporting events have become an important issue. Meanwhile, soccer is of particular importance in Iran and the need for a model that, not only can predict the result of a game, but also can predict the result of an entire league, is felt. Therefore, this paper attempts to present a statistical model for responding to this need by using Bayesian methods. Here, Poisson regression model is first introduced and then is used to obtain estimates of model parameters, using WinBUGS software. Finally, the model is used to fit on the data of the Iranian Premier League during the year 1388-89.