Since the grade of elements in a mining area has spatial correlation, its statistical analysis is impossible with the usual statistical methods. Therefore, spatial statistics methods are used in their analysis to model the spatial correlation structure and predict the unknown grade values in arbitrary locations. For prediction, including dependence structures and trend following due to contextual factors (such as topography) helps improve the accuracy of response variable forecasting. In data analysis, the small number of observations, the presence of outlier observations, or data with a highly skewed distribution, causes an inaccurate estimation of the variogram. In this article, a bayesian approach is used for the 3D modeling of the data, and an approximate bayesian method known as Integrated Nested Laplace Approximations (INLA) is used to fit the proposed model. Since Geostatistical data are densely indexed, to ensure fast calculations using INLA, the spatial model defined on the study area was converted into a Gaussian Markov Random Field (GMRF) using triangulation and the Stochastic Partial Differential Equation (SPDE) approach. The implementation of the INLA+SPDE method on a 3D Geostatistical data set is a new topic in the field of mining data modeling.

Introduction: One of the vital skills which has an impact on emotional health and well-being is the regulation of emotions. In recent years, the neural basis of this process has been considered widely. One of the powerful tools for eliciting and regulating emotion is music. The Anterior Cingulate Cortex (ACC) is part of the emotional neural circuitry involved in Major Depressive Disorder (MDD). The current study uses functional Magnetic Resonance Imaging (fMRI) to examine how neural processing of emotional musical auditory stimuli is changed within the ACC in depression. Statistical inference is conducted using a bayesian Generalized Linear Model (GLM) approach with an Integrated Nested Laplace Approximation (INLA) algorithm. Methods: A new proposed bayesian approach was applied for assessing functional response to emotional musical auditory stimuli in a block design fMRI data with 105 scans of two healthy and depressed women. In this bayesian approach, Unweighted Graph-Laplacian (UGL) prior was chosen for spatial dependency, and autoregressive (AR) (1) process was used for temporal correlation via pre-weighting residuals. Finally, the inference was conducted using the Integrated Nested Laplace Approximation (INLA) algorithm in the R-INLA package. Results: The results revealed that positive music, as compared to negative music, elicits stronger activation within the ACC area in both healthy and depressed subjects. In comparing MDD and Never-Depressed (ND) individuals, a significant difference was found between MDD and ND groups in response to positive music vs negative music stimuli. The activations increase from baseline to positive stimuli and decrease from baseline to negative stimuli in ND subjects. Also, a significant decrease from baseline to positive stimuli was observed in MDD subjects, but there was no significant difference between baseline and negative stimuli. Conclusion: Assessing the pattern of activations within ACC in a depressed individual may be useful in retraining the ACC and improving its function, and lead to more effective therapeutic interventions.

Animal models are used to model the observations of animal performance that are genetically dependent. These models are considered as generalized linear mixed models and the genetic correlation structure of data is considered through random effects of breeding values. One goal of the mentioned models is to estimate variance components. In this research, an approximate bayesian approach presented to estimate variance components in animal model and compared with the conventional bayesian approach. A generated data set for hypothetical animal population with 1084 records was used. The observations are the animal's birth weight and the data includes dam ID, sire ID, sex and birth year. The effect of gender was considered as fixed effect and the effects of dam, animal and year of birth were used as random effect. Four different models were fitted by the conventional bayesian approach and the appropriate model was selected by deviance information criteria. The approximate bayesian approach was applied on it. Time consuming with a PC with configuration (Intel Core i7, 4GB, 2. 7 GHz) was about 120 second for the conventional bayesian approach and little than 10 second for the approximate bayesian approach. Goodness of fit was computed by relative root mean squared error of prediction that was respectively 0. 1568 and 0. 1499 for conventional bayesian and the approximate bayesian approaches. T-test was used to illustrate lack of significant different to fit weight of animals between two approaches. The null hypothesis was accepted with p-value 0. 98 that it shows mean of fitted animal weights for two approaches are equal.

Hierarchical spatio-temporal models are used for modeling space-time responses and temporally and spatially correlations of the data is considered via Gaussian latent random field with Matérn covariance function. The most important interest in these models is estimation of the model parameters and the latent variables, and is predict of the response variables at new locations and times. In this paper, to analyze these models, the bayesian approach is presented. Because of the complexity of the posterior distributions and the full conditional distributions of these models and the use of Monte Carlo samples in a bayesian analysis, the computation time is too long. For solving this problem, Gaussian latent random field with Matern covariance function are represented as a Gaussian Markov Random Field (GMRF) through the Stochastic Partial Differential Equations (SPDE) approach. Approximatin Baysian method and Integrated Nested Laplace Approximation (INLA) are used to obtain an approximation of the posterior distributions and to inference about the model. Finally, the presented methods are applied to a case study on rainfall data observed in the weather stations of Semnan in 2013.

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In some instances, the occurrence of an event can be influenced by its spatial location, giving rise to spatial survival data. The accurate and precise estimation of parameters in a spatial survival model poses a challenge due to the complexity of the likelihood function, highlighting the significance of employing a bayesian approach in survival analysis. In a bayesian spatial survival model, the spatial correlation between event times is elucidated using a geostatistical model. This article presents a simulation study to estimate the parameters of classical and spatial survival models, evaluating the performance of each model in fitting simulated survival data. Ultimately, it is demonstrated that the spatial survival model exhibits superior efficacy in analyzing blood cancer data compared to conventional models.

In this paper we briefly review some of the bayesian techniques for sample size determination in different trials. The two main areas are inferential and decision theoretic frameworks. In the inferential approach we are usually concerned with inference about unknown parameter(s) of interest and sample sizes are determined by taking the parameters of posterior distribution into account. In the decision theoretic approach the problem is treated as a decision problem and using a proper utility function the optimal sample size is determined by optimizing an objective function

Education is a continually developing activity, it shapes the way an individual develops their attitudes, thoughts and behavior. Education is not just about being literate but it is an overall development of the person in every aspect of their lives. A society's educational system can be greatly linked to its culture as the culture that one is in have an affect on the type of curriculum the institutions will develop in order to cater to every individual of that particular culture. Education moulds and shapes a society and it is influenced by the culture of the particular country or society. The educational system acts as a point of reference for the society's needs and demands. The principles that dominate this article is culture and education are interrelated and interconnected. Every educational paradigm is influenced by the culture of the society in which it operates. This essay underlines the extensive connection between culture and education. As a result, the goal of this essay is to depict these features from a Philosophical-Psychological approach.