Probability Theory and Statistical Analysis

2014 ◽  
pp. 61-68
2009 ◽  
Vol 55 ◽  
pp. 121-138
Author(s):  
John Kingman

David Kendall was the father of modern probability theory in Britain, a powerful and scholarly mathematician equally at home in abstract theory and in perceptive applications to diverse fields. Through his own research, his influence on generations of students, and from 1962 his leadership of the Cambridge Statistical Laboratory, he inspired the parallel developments of stochastic analysis and applied probability, as well as the statistical analysis of complex structured data.


PMLA ◽  
2015 ◽  
Vol 130 (1) ◽  
pp. 129-142 ◽  
Author(s):  
M. L. Gasparov ◽  
Michael Wachtel

Mikhail Leonovich Gasparov (1935-2005) was one of the greatest and most prolific russian literary scholars of the twentieth century.Though associated with the Moscow-Tartu school of semiotics, Gasparov's writings were so diverse and multifaceted—and his scholarly personality so distinct—as to elude categorization.Gasparov's accomplishments are all the more remarkable when measured against the rigid Marxist-Leninist paradigms that ruled humanities education and scholarship in the Soviet Union. A philologist with a special interest in verse form, he managed to sidestep the procrustean bed of Soviet ideology, building instead on the barely tolerated work of the Russian formalists and structuralists. He embraced and developed their goal of turning literary study into an exact science by applying statistical analysis and probability theory to poetics. Gasparov's scholarship was based on unprecedented amounts of data, which he painstakingly compiled in the precomputer era. However, he was never satisfied with the data as such; he used them to reach profound and unexpected conclusions.


Diagnosis ◽  
2021 ◽  
pp. 6-23
Author(s):  
Ashley Graham Kennedy

This chapter demonstrates how the process of clinical diagnosis requires first establishing a therapeutic alliance between the patient and the physician and then drawing on, and evaluating, both qualitative and quantitative forms of evidence. Although clinical diagnosis is a technical process that requires an understanding of scientific study design, probability theory, and statistical analysis, it is also relational because it starts with the relationship between the patient and the physician. In fact, very often, getting to a correct diagnosis directly depends on the way in which the physician navigates this relationship: If a physician is dismissive of the patient’s concerns, the physician risks cutting the patient off too quickly and possibly missing important pieces of evidence that could lead to a timely and accurate diagnosis.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruiqin Su ◽  
Mohammed Basheri ◽  
Mohamad Salama

Abstract The paper relies on the practice process of entrepreneurship education in colleges and universities to carry out research. It summarizes the optimal models suitable for students’ employment and entrepreneurship based on statistical analysis using the probability theory. We built a pyramid structured model of college students’ entrepreneurial ability based on this model. The thesis uses statistical methods to carry out a factor analysis of various abilities. It provides a basis for the adjustment and implementation of employment-oriented entrepreneurship education models in colleges and universities.


Author(s):  
Robin Whitty ◽  
Robin Wilson

Alan Turing’s mathematical interests were deep and wide-ranging. From the beginning of his career in Cambridge he was involved with probability theory, algebra (the theory of groups), mathematical logic, and number theory. Prime numbers and the celebrated Riemann hypothesis continued to preoccupy him until the end of his life. As a mathematician, and as a scientist generally, Turing was enthusiastically omnivorous. His collected mathematical works comprise thirteen papers, not all published during his lifetime, as well as the preface from his Cambridge Fellowship dissertation; these cover group theory, probability theory, number theory (analytic and elementary), and numerical analysis. This broad swathe of work is the focus of this chapter. But Turing did much else that was mathematical in nature, notably in the fields of logic, cryptanalysis, and biology, and that work is described in more detail elsewhere in this book. To be representative of Turing’s mathematical talents is a more realistic aim than to be encyclopaedic. Group theory and number theory were recurring preoccupations for Turing, even during wartime; they are represented in this chapter by his work on the word problem and the Riemann hypothesis, respectively. A third preoccupation was with methods of statistical analysis: Turing’s work in this area was integral to his wartime contribution to signals intelligence. I. J. Good, who worked with Turing at Bletchley Park, has provided an authoritative account of this work, updated in the Collected Works. By contrast, Turing’s proof of the central limit theorem from probability theory, which earned him his Cambridge Fellowship, is less well known: he quickly discovered that the theorem had already been demonstrated, the work was never published, and his interest in it was swiftly superseded by questions in mathematical logic. Nevertheless, this was Turing’s first substantial investigation, the first demonstration of his powers, and was certainly influential in his approach to codebreaking, so it makes a fitting first topic for this chapter. Turing’s single paper on numerical analysis, published in 1948, is not described in detail here. It concerned the potential for errors to propagate and accumulate during large-scale computations; as with everything that Turing wrote in relation to computation it was pioneering, forward-looking, and conceptually sound. There was also, incidentally, an appreciation in this paper of the need for statistical analysis, again harking back to Turing’s earliest work.


Diagnosis ◽  
2021 ◽  
pp. 125-126
Author(s):  
Ashley Graham Kennedy

This concluding chapter reiterates the point that being a good diagnostician requires not only an understanding of probability theory and statistical analysis but also learning to listen to your patients, learning how to interpret the results of diagnostic tests by taking into account clinical considerations, learning how to manage and communicate diagnostic uncertainty in the clinical setting, understanding the potential reasons to conduct diagnostic tests or not, and being concerned with issues of diagnostic justice while keeping in mind the concerns of the actual patient who is in front of you.


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