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in this paper, we discuss generating a random sample from gamma distribution using generalized exponential distribution.

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‎The purpose of this study was to determine and evaluate of spatial distribution of gold and silver elements concentration by using geostatistical methods‎. ‎This study was carried out in Ghezel Ozen area for 95 samples of lithogeochemicals‎. ‎At first‎, ‎Censor data was replaced and the values of outlier's data were identified using the box-Plot and Q-Q-Plot charts and reduced by the Doerffel method‎. ‎Finally‎, ‎the data were normalized using logarithmic transformations and then the geostatistical analysis was used‎. ‎Variogram studies showed that the spherical model is the best fitted model and the spatial correlation range for the two elements of Au and Ag were approximately 2500 m‎. ‎Finally‎, ‎the estimation and estimation variance maps of the studied elements were prepared by using ordinary kriging geostatistical method with the spherical model on the GS‎+ ‎software‎. ‎Evaluating the results by calculating the root mean square error (RMSE) and calculating the mean absolute error (MAE) indicates the acceptable accuracy of variogram model‎. ‎By studying the kriging estimation and kriging estimation variance maps‎, ‎the anomal regions were introduced for the elements of Au and Ag in the case study‎. 

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The current article is a translation of a paper published in Significance journal, 2020, Vol. 17, No. 4 captioned as “C.R. Rao’s ‎Century''‎, which has been scripted as a perception of an appreciation  note by involvements of  Bradley Efron, Shun-ichi Amari, Donald B. Rubin, Arni S. R. Srinivasa Rao and David R. Cox. Therefore, it is not possible to address this manuscript as a scientific paper, which is regularly accepted among the researchers. Evidently, the proposed translated article is prepared with the focus on appreciating professor Rao’s a century contribution in statistics. With this intention, Persian speakers, i.e., those who are somehow associated with statistics, could become aware of professor Rao’s invaluable role in spreading the statistics around the world. Absolutely, individuals that have achieved a bachelor’s degree in statistics, are familiar with at least two well-known titles: “Cramer-Rao’s inequality'' and “Rao-Blackwell theorem'', possessing Rao’s designation in both titles. Needless to say, that mentioning his remarkable role in statistics and learning more about his outstanding character by some renowned statisticians, who have also made remarkable impacts on statistics, is a must. In accordance with the author of this paper, advantageous activities of Rao, some of which have come to this paper, can be considered as a model for those who enter in various fields of statistics and intend to follow Rao’s scientific and social life.

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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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The Buffon’s needle problem is a random experiment leading to estimate of the number π by ”randomly” throwing a
needle onto a plane partitioned by parallel lines. Indeed, in the independently repetitions of the experiment, based on
the number of times where the needle will cross a line, one can construct an estimator of π. The aim of this note is to
obtain a better estimator (in some sense) by considering a model where the plane is partitioned by rectangles. We show
that both estimators are asymptotically normal and unbiased; and also the confidence intervals are obtained for π. We
calculate the asymptotic relative efficiency of the estimators and show that the estimator based on the rectangles is more
efficient. The data of a real experiment is provided.

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In this article, we take a look at Henri Poincaré's view on the methodology of mathematics, taken from the book Science and Method, and look at the role of choosing facts in the discovery of mathematics. The author deals with the foundations of the methodology of science and beautifully explains the future of mathematics and the direction of the development of mathematics, which started from the past and is continuing, and impresses the reader with this deep thinking. The author's belief in the framework of discovery of mathematical rules based on facts is well evident in this book. Poincaré's profound thinking in studying the laws of chance and its hidden realities in relation to the facts of existence is undeniable. This short article presents a selection of the content of the first part of the book to get familiar with it.