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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.

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Often, in sample surveys, respondents refused to answer some questions of a sensitive nature. Randomized response methods are designed not to reveal respondent confidentiality. In this article, a new quantitative randomized response method is introduced, and by conducting a series of simulation studies, we show that the proposed method is preferable to the cumulative and multiplicative methods. By using unbiased predictors, we estimate the covariance between two sensitive variables. In an experimental study using the proposed method, the average number of cheating and the average daily cigarette consumption of the Shahid Chamran University of Ahvaz students are estimated along with their variance, and an estimate for the covariance between them is provided.

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Systemic risk, as one of the challenges of the financial system, has attracted special attention from policymakers, investors, and researchers. Identifying and assessing systemic risk is crucial for enhancing the financial stability of the banking system. In this regard, this article uses the Conditional Value at Risk method to evaluate the systemic risk of simulated data and Iran's banking system. In this method, the conditional mean and conditional variance are modeled using Autoregressive Moving Average and Generalized Autoregressive Conditional Heteroskedasticity models, respectively. The data studied includes the daily stock prices of 17 Iranian banks from April 8, 2019, to May 1, 2023, which contains missing values in some periods. The Kalman filter approach has been used for interpolating the missing values. Additionally, Vine copulas  with a hierarchical tree structure have been employed to describe the nonlinear dependencies and hierarchical risk structure of the returns of the studied banks. The results of these calculations indicate that Bank Tejarat has the highest systemic risk, and the increase in systemic risk, in addition to causing financial crises, has adverse effects on macroeconomic performance. These results can significantly help in predicting and mitigating the effects of financial crises and managing them effectively.