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The Internet of Things is suggested as the upcoming revolution in the Information and communication technology due to its very high capability of making various businesses and industries more productive and efficient. This productivity comes from the emergence of innovation and the introduction of new capabilities for businesses. Different industries have shown varying reactions to IOT, but what is clear is that IOT has applications in all Businesses. These applications have made significant progress in some industries such as health and transportation but is under development in others, namely agriculture and animal husbandry. In fact, the production of data bases on the Internet of Things is one of the main pillars in the field of big data and data science, Therefore, statistical concepts and models that are used in data science can be beneficially implemented in such data. Among the valid statistical models, Bayesian statistics for data is being utilized in these studies. In this research the fundamentals of Bayesian statistics for big data and most notably the data produced by IOT is explained. They have been Pragmatically examined in both road traffic as well as people’s social behavior towards using vehicles, which have had practically and scientifically valid results.
 

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Thomas Bayes, the founder of Bayesian vision, entered the University of
Edinburgh in 1719 to study logic and theology. Returning in 1722, he worked with
his father in a small church. He also was a mathematician and in 1740 he made a
novel discovery which he never published, but his friend Richard Price found it in his
notes after his death in 1761, reedited
it and published it. But until Laplace, no one
cared until the late 18th century, when data did not have equal confidence in Europe.
Pierre − Simon Laplace, a young mathematician, believed that probability theory was
a key in his hand, and he independently discovered the Bayesian mechanism and published
it in 1774. Laplace expressed the principle not in an equation but in words.
Today, Bayesian statistics as a discipline of statistical philosophy and the interpretation of probability is very important and has become known as the Bayesian theorem
presented after Bayesian death. Allen Turing is a British computer scientist, mathematician
and philosopher who is now known as the father of computer science and artificial
intelligence. His outstanding achievements during his short life are the result of the
adventures of a beautiful mind that was finally extinguished forever with a suspicious
death. During World War II, Turing worked in Belchley Park, the center of the British
decipherment, and for a time was in charge of the German Navy’s cryptographic analysis.
He devised several methods, specifically from Bayesian’s point of view, without
breaking his name to crack German codes, as well as the electromechanical machine
method that could find the features of the Enigma machine. Finding Enigma can also
be considered one of his great works. Alan Turing was a leading scientist who played
an important role in the development of computer science and artificial intelligence and
the revival of Bayesian thought. Turing provided an effective and stimulating contribution
to artificial intelligence through the Turing experiment. He then worked at the
National Physics Laboratory in the United Kingdom, presenting one of the prototypes
of a stored computer program, though it worked, which was not actually made as the
”Manchester Mark ”. He went to the University of Manchester in 1948 to be recognized
as the world’s first real computer. However, later on, the role of Bayesian rule and law
in scientific developments becomes more important. Many possible Bayesian methods
in the 21st century have made significant advances in the explanation and application of
Bayesian statistics in climate development and have solved many of the world’s problems.
New global technology has grown on Bayesian ideas, which will be reviewed intion of probability is very important and has become known as the Bayesian theorem
presented after Bayesian death. Allen Turing is a British computer scientist, mathematician
and philosopher who is now known as the father of computer science and artificial
intelligence. His outstanding achievements during his short life are the result of the
adventures of a beautiful mind that was finally extinguished forever with a suspicious
death. During World War II, Turing worked in Belchley Park, the center of the British
decipherment, and for a time was in charge of the German Navy’s cryptographic analysis.
He devised several methods, specifically from Bayesian’s point of view, without
breaking his name to crack German codes, as well as the electromechanical machine
method that could find the features of the Enigma machine. Finding Enigma can also
be considered one of his great works. Alan Turing was a leading scientist who played
an important role in the development of computer science and artificial intelligence and
the revival of Bayesian thought. Turing provided an effective and stimulating contribution
to artificial intelligence through the Turing experiment. He then worked at the
National Physics Laboratory in the United Kingdom, presenting one of the prototypes
of a stored computer program, though it worked, which was not actually made as the
”Manchester Mark ”. He went to the University of Manchester in 1948 to be recognized
as the world’s first real computer. However, later on, the role of Bayesian rule and law
in scientific developments becomes more important. Many possible Bayesian methods
in the 21st century have made significant advances in the explanation and application of
Bayesian statistics in climate development and have solved many of the world’s problems.
New global technology has grown on Bayesian ideas, which will be reviewed in this article.

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Today, the diagnosis of diseases using artificial intelligence and machine learning algorithms are of great importance, because by using the data available in the study field of the desired disease, useful information and results can be obtained that reduce the occurrence of many deaths. Among these diseases, we can mention the diagnosis of diabetes, which has spread today due to the growth of urban life and the decrease in people's activity. So, it is very important to know whether a person is suffering from diabetes or not. In this article, the data set related to the information of people who have done the diabetes diagnosis test is used, this information is related to 520 people. People are classified into two groups based on whether their diabetes test result is positive or not, and Bayesian classification methods such as Bayesian Support Vector Machine, Naive Bayes, CNK and CatBoost ensemble classification method have been used to conclude which of these The methods can have a better ability to analyze the data and also to compare these methods use accuracy, precision, F1-score, recall, ROC diagram.

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In ‎this ‎article, ‎queunig ‎model ‎‎$‎M/M/1‎$ ‎is ‎Considered, ‎in ‎which ‎the ‎innterarrival ‎of ‎customers ‎have ‎an ‎exponenial ‎disributon ‎with ‎the ‎parameter ‎‎$‎lambda‎$ ‎and ‎the ‎service ‎times‎ ‎have ‎an ‎exponenial ‎disributon with the ‎parameter ‎‎$‎mu‎$ ‎and ‎are ‎independent ‎of ‎the ‎interarrival ‎times.‎ ‎it ‎is ‎also ‎assumed ‎that ‎the ‎system ‎is ‎active ‎until ‎‎$‎T‎$‎. Then, under this stopping time Bayesian, ‎$‎E‎$‎-Bayesian and hierarchical Bayesian estima‎‏‎tion‎s of the traffic intensity parameter of this queuing model are obtained under the general entropy loss function and considering the gamma and erlang prior distributions for parameters ‎$‎lambda‎$ ‎and ‎‎$‎mu‎$‎, respicctively. Then, using numerical analysis and based on a new index, Bayesian, ‎$‎E‎$‎-Bayesian and hierarchical Bayesian estima‎‏‎tion‎s are compared.

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The statistical inference of the multi-component stress-strength parameter, $R_{s,k}$, is considered in the three-parameter Weibull distribution. The problem is studied in two cases. In the first case, assuming that the stress and strength variables have common shape and location parameters and non-common scale parameters and all these parameters are unknown, the maximum likelihood estimation and the Bayesian estimation of the parameter $‎R_{s,k}$ are investigated. In this case, as the Bayesian estimation does not have a closed form, it is approximated by two methods, Lindley and $mbox{MCMC}$. Also, asymptotic confidence intervals have been obtained. In the second case, assuming that the stress and strength variables have known common shape and location parameters and non-common and unknown scale parameters, the maximum likelihood estimation, the uniformly minimum variance unbiased estimators, the exact Bayesian estimation of the parameter $‎R_{s,k}$ and the asymptotic confidence interval is calculated. Finally, using Monte Carlo simulation, the performance of different estimators has been compared.

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Marshall-Olkin introduced a family of distributions which obtained by adding a parameter into other distributions. Santoz-Neto etal study an extended Weibull distribution. In this paper two Raiyle and Pareto extended weibull are studied under some momemts and Bayesian methods with some loss functions such as squared error, entropy, linex, squared error in logarithm and modified linex. Also the MCMC method are study for these two distributions.

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    We live in the information age, constantly surrounded by vast amounts of data from the world around us. To utilize this information effectively, it must be mathematically expressed and analyzed using statistics.
     Statistics play a crucial role in various fields, including text mining, which has recently garnered significant attention. Text mining is a research method used to identify patterns in texts, which can be in written, spoken, or visual forms.
      The applications of text mining are diverse, including text classification, clustering, web mining, sentiment analysis, and more. Text mining techniques are utilized to assign numerical values to textual data, enabling statistical analysis.
       Since working with data requires a solid foundation in statistics, statistical tools are employed in text analysis to make predictions, such as forecasting changes in stock prices or currency exchange rates based on current textual data. 
       By leveraging statistical methods, text mining can uncover, confirm, or refute the truths hidden within textual content. Today, this topic is widely used in machine learning. This paper aims to provide a basic understanding of statistical tools in text mining and demonstrates how these powerful tools can be used to analyze and interpret events.