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.

Magnetic Resonance Imaging (MRI) is a notable medical imaging technique that is based on Nuclear Magnetic Resonance (NMR). MRI is a safe imaging method with high contrast between soft tissues, which made it the most popular imaging technique in clinical applications. MR Imagechr('39')s visual quality plays a vital role in medical diagnostics that can be severely corrupted by existing noise during the acquisition process. Therefore, the denoising of these images has great importance in medical applications. During the last decades, lots of MR denoising APPROACHes from various groups of techniques have been proposed that can be classified into two general groups of acquisition-based noise reduction and post-acquisition denoising methods. The first groupchr('39')s APPROACHes will add imaging time and led to a much time-consuming process. The second groupchr('39')s issues are its complicated mathematical equations required for image denoising, in which stochastic algorithms are usually required to solve these complex equations. This study aims to find an appropriate statical post-acquisition denoising MR imaging method based on the BAYESIAN technique. Finding the appropriate prior density function also has great importance since the BAYESIAN techniquechr('39')s performance is related to its prior density function. In this study, the uniform distribution has been applied as the prior density function. The prior uniform distribution function will reduce the BAYESIAN algorithm to its simplest possible state and lower computational complexity and time consumption. The proposed method can solve the numerical problems with an adequate timing process without complex algorithms and remove noise in less than 120 seconds on average in all cases. To quantitatively assess image improvement, we used the Structural Similarity Function (SSIM) in MATLAB. The similarity with this function shows an average improvement of more than 0. 1 in all images. Considering the results, it can be concluded that combining the uniform distribution function as a prior density function and the BAYESIAN algorithm can significantly reduce the imagechr('39')s noise without the time and computational cost.

Artifacts are ubiquitous and influential in our world, but their nature and existence are controversial. Several theories have been proposed to explain the ontology of artifacts. Drawing on Popper's theory of three worlds, this paper suggests a metaphysics for artifacts along the line of a critical rationalist (CR) APPROACH. This theory distinguishes between three realms of reality: the physical world (World 1), the mental world (World 2), and the world of objective knowledge (World 3). The paper argues that artifacts have different ontological components that correspond to these three realms, and that each component is real and causal. The paper shows how this perspective can account for the intentional and functional aspects of artifacts, as well as their dependence on plans that influence different realms of reality. The paper explains how this pluralistic ontology, compared to the rival theories, enables one to explain the relevant ontological problems of artifacts. The paper also explores how this proposal can lead to a research program encompassing a broader range of technologies, such as social artifacts. In sum, the paper suggests that Popper's three worlds theory provides a rich and comprehensive framework for understanding the metaphysics of artifacts.

Control chart pattern (CCP) recognition techniques are widely used to identify the potential process problems. Recently, artificial neural network (ANN) –based techniques are popular for this problem. However, finding the suitable architecture of an ANN-based CCP recognizer and its training process are time consuming and the obtained results are not interpretable. To facilitate the research gap, this paper presents a simple statistical APPROACH for detecting and identifying control chart patterns. In this method, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the Beliefs on each pattern is determined. Then using Bayes’ rule, Beliefs are updated recursively. Finally, when the amount of a derived statistic falls outside the calculated control interval a pattern recognition signal is issued. The advantage of this APPROACH comparing with other existing CCP recognition methods is that it has no need for training. Simulation results show high accuracy and satisfactory speed of the proposed method.

AbstractBanking crises are occurring intermittently. This indicates that pre-current warning models have not been successful in identifying these crises. Examination of existing models specifies that the failure of these models is mainly due to the identification of explanatory variables and experimental design of the model, which the researchers of the present study aimed at improving. In order to moderate the problem of model uncertainty by averaging all models (BAYESIAN averaging) the present research attempted to determine the factors affecting the banking crisis in Iran. In this study, 49 variables affecting the banking crisis were included in the model. Finally, using the BAYESIAN averaging model APPROACH, 12 non-fragile variables affecting the financial crisis were identified consisting of cost of funding, none performing loan (NPL), deposit to loan (DTL), spread, capital adequacy, earning assets to total assets ratio, net LTD (after deducted Legal reserves), cash coverage ratio, net stable funding ratio (NSFR) in the presence of all variables, duration of assets and liabilities, interest rate duration, and increase in properties' possession. According to the results, it could be deduced that the banking crisis index in the Iranian economy is a problem with wide dimensions as the variables related to monetary and financial sector policy makers affect this index. The banks studied in this study are 10 banks listed on the Tehran Stock Exchange (Kar Afarin, Eghtesad-e Novin, Parsian, Sina, Mellat, Tejarat, Saderat, Post Bank, Mellat, Dey) in an 11-year period from 2008 to 2019.

Introduction Studying crime data has become one of the essential topics in the world due to its connection with human security. Analyzing this type of data can effectively prevent future crimes and identify spatial patterns and factors that facilitate the commission of crimes to control crime-prone areas. Most of the time, crime data has a spatio-temporal structure that causes the formation of different spatio-temporal patterns. Therefore, spatio-temporal monitoring of crime data is essential in identifying factors that cause crime and preventing crime. An important issue in many cities is related to crime events, and the spatio-temporal BAYESIAN APPROACH leads to identifying crime patterns and hotspots. In BAYESIAN analysis of spatio-temporal crime data, there is no closed form for posterior distribution because of its non-Gaussian distribution and the existence of latent variables. In this case, we face challenges such as high dimensional parameters, extensive simulation and time-consuming computation in applying MCMC methods. Material and Methods In this paper, we apply INLA to analyze crime data in Colombia. To describe the above concepts, a three-stage hierarchical model is considered. The advantages of this method can be the estimation of criminal events at a specific time and location and exploring unusual patterns in places. Results and Discussion The BAYESIAN analysis of crime data is usually performed as BAYESIAN infer ence of pure spatial or temporal patterns. However, such spatial or temporal BAYESIAN analyses are not suitable for crime data. In this article, in a case study, BAYESIAN hierarchical spatio-temporal analysis of crime data in Colombia was discussed using the INLA APPROACH, which considers spatio-temporal dependence and makes the model more flexible in detecting unusual patterns. Exploratory data analysis is also discussed, detecting areas with unusual behaviour over time. Four different models were fitted to the data, and the best model that includes spatio-temporal interaction was selected using the DIC criterion. The research results identify the most important centre of crime in the Kennedy area of Bogotá, , as well as the highest crime rate in the time frame. Then, hierarchical spatio-temporal BAYESIAN analysis of these data was done with the INLA APPROACH. Conclusion The advantage of using this BAYESIAN APPROACH is that it includes the effects of spatio-temporal correlation in the model and makes the model flexible in detecting areas with abnormal behaviour over time and in different places. For this purpose, four different models, including side effects and spatio-temporal combination, were fitted to the crime data. The best model, including the spatio-temporal interaction effect, was proposed using the deviance information criterion. The comprehensive and scientific comparison of the two BAYESIAN methods INLA and the MCMC algorithm in terms of accuracy, speed and even accessibility and convenient use for researchers requires independent scientific and practical research because, for example, the various methods of sampling in the MCMC algorithms and sometimes its different methods in INLA make it difficult to compare accuracy. How to use parallel calculations in the application of these two methods is also effective in comparing the speed, and simply comparing the outputs cannot express the advantage of one method over the other.