This paper introduces a novel semiparametric Bayesian approach for bivariate meta-regression. The method extends traditional binomial models to trinomial distributions, accounting for positive, neutral, and negative treatment effects. Using a conditional Dirichlet process, we develop a model to compare treatment and control groups across multiple clinical centers. This approach addresses the challenges posed by confounding factors in such studies. The primary objective is to assess treatment efficacy by modeling response outcomes as trinomial distributions. We employ Gibbs sampling and the Metropolis-Hastings algorithm for posterior computation. These methods generate estimates of treatment effects while incorporating auxiliary variables that may influence outcomes. Simulations across various scenarios demonstrate the model’s effectiveness. We also establish credible intervals to evaluate hypotheses related to treatment effects. Furthermore, we apply the methodology to real-world data on economic activity in Iran from 2009 to 2021. This application highlights the practical utility of our approach in meta-analytic contexts. Our research contributes to the growing body of literature on Bayesian methods in meta-analysis. It provides valuable insights for improving clinical study evaluations.

A hierarchical Bayes model for analysis of contingency tables is presented and, using it, inference about correlation parameter, logarithm of the odds ratio, is studied. For drawing random sample from the distribution of logarithm of the odds ratio, computational method of Gibbs sampling is used. For testing independence the use of the hierarchical Bayes in calculating the Bayes factor is introduced and in an applied example is employed.

This paper addresses the problem of Bayesian estimation of the parameters, reliability and hazard function in the context of record statistics values from the two-parameter Lomax distribution. The ML and the Bayes estimates based on records are derived for the two unknown parameters and the survival time parameters, reliability and hazard functions. The Bayes estimates are obtained based on conjugate prior for the scale parameter and discrete prior for the shape parameter of this model. This is done with respect to both symmetric loss function (squared error loss), and asymmetric loss function (linear-exponential (LINEX)) loss function. The maximum likelihood and the different Bayes estimates are compared via Monte Carlo simulation study. A practical example consisting of real record values including in the data from an accelerated test on insulating fluid reported by Nelson was used for illustration and comparison. Finally, Bayesian predictive density function, which is necessary to obtain bounds for predictive interval of future record is derived and discussed using a numerical example.

This article examines statistical inference for R= P(Y<X) where X and Y are independent but not identically distributed Pareto of the first kind (Pareto (I)) random variables with same scale parameter but different shape parameters. The Maximum likelihood, uniformly minimum variance unbiased and Bayes estimators with Gamma prior are used for this purpose. Simulation studies which compare the estimators are presented. Moreover, sensitivity of Bayes estimator to the prior parameters is considered.

Industries' increasing progress and complexity has made maintenance and repair tasks very challenging, complex, and time-consuming. Maintenance is one of the important sectors in several industries, and improvement in this sector can have excellent results. This paper develops a new maintenance prediction model based on Bayesian networks (BN) capabilities. The models include several variables that experts determine and their influence on each other's-called conditional probability tables-which are learned from historical data. The model is implemented in an automobile repair department case study to show its performance. The model is evaluated through a sensitivity analysis, and the results show the proficiency of the proposal mode.

In this paper, we show that the problem of grammar induction could be modeled as a combination of several model selection problems. We use the infinite generalization of a Bayesian model of cognition to solve each model selection problem in our grammar induction model. This Bayesian model is capable of solving model selection problems, consistent with human cognition. We also show that using the notion of history-based grammars will increase the number and decrease the complexity of model selection problems in our grammar induction model. This results in the induction of a better grammar which leads to 9.1 points increase in F1 measure, for parsing the section 22 of Penn treebank in comparison with a similar model that does not use history-based grammar induction techniques.

This paper considers the Bayesian model averaging of inverse Gaussian regression models for regression analysis in situations that the response observations are positive and right-skewed. The computational challenges related to computing the essential quantities for executing of this methodology and their dominating ways are discussed. Providing closed form expressions for the interested posterior quantities and considering suitable prior distributions are two attractive aspects of the proposed methodology. The proposed approach has been evaluated via a simulation study, and its applicability is expressed by using a real example related to the seismic studies.

Social networks, networks that have come into existence, are on the Internet, whose purpose of the establishment is to communicate with different people from different societies. Social networks are a developed form whose information is not trusted by all individuals. Although, it is a popular network that can provide trusted information for some people. If one or more users receive some information from oth-ers, they should assure they have not recieved incorrect data from malicious users. Solutions to these prob-lems are confidence models. Provided that trust deals withpossibilities, Bayesian networks use possibilities to solve problems. As a result, the Bayesian network can improve the calculation of trust. In this study, the proposed model (BTSN) presents a model for calculating confidence using Bayesian networks for social networking. This model is able to calculate the confidence accurately and, in a large scale, can be used in social networks. In addition, the the performance and methods have been studied.

Recent crises indicate the failure of early warning models. The research considers this failure to identify the explanatory variables and the empirical design of the model, the factors that this research seeks to improve. In this research, it is attempted to determine the factors affecting the financial crisis in Iranian economy by defining uncertainty in crisis models and using a conventional approach to Bayesian average. In this study, 62 variables affecting the financial crisis were introduced into the model. Finally, using the Bayesian averaging model, 12 non-critical variables that affect the financial crisis, which include deficit or surplus, unofficial exchange rate deviation from the official, inflation rate, ratio External debt to foreign assets of the Central Bank; Increasing coefficient of money (liquidity/ monetary base); Export to GDP ratio; Import to GDP; Government expenditure to GDP ratio; Budget deficit to GDP; Liquidity ratio to foreign assets of Central Bank; Rate of credit growth granted to the private sector and inflation squeeze. Regarding the output of the results, it can be stated that the financial crisis index in Iran's economy is a multi-dimensional problem, as variables related to financial policy, monetary policy and foreign exchange policy affect this index.