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Review of Univariate Probability

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Probability for Statistics and Machine Learning

Part of the book series: Springer Texts in Statistics ((STS))

Abstract

Probability is a universally accepted tool for expressing degrees of confidence or doubt about some proposition in the presence of incomplete information or uncertainty. By convention, probabilities are calibrated on a scale of 0 to 1; assigning something a zero probability amounts to expressing the belief that we consider it impossible, whereas assigning a probability of one amounts to considering it a certainty. Most propositions fall somewhere in between. Probability statements that we make can be based on our past experience, or on our personal judgments. Whether our probability statements are based on past experience or subjective personal judgments, they obey a common set of rules, which we can use to treat probabilities in a mathematical framework, and also for making decisions on predictions, for understanding complex systems, or as intellectual experiments and for entertainment. Probability theory is one of the most applicable branches of mathematics. It is used as the primary tool for analyzing statistical methodologies; it is used routinely in nearly every branch of science, such as biology, astronomy and physics, medicine, economics, chemistry, sociology, ecology, finance, and many others. A background in the theory, models, and applications of probability is almost a part of basic education. That is how important it is.

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References

  • Alon, N. and Spencer, J. (2000). The Probabilistic Method, Wiley, New York.

    Book  MATH  Google Scholar 

  • Ash, R. (1972). Real Analysis and Probability, Academic Press, New York.

    Google Scholar 

  • Barbour, A. and Hall, P. (1984). On the rate of Poisson convergence, Math. Proc. Camb. Phil. Soc., 95, 473–480.

    Article  MathSciNet  MATH  Google Scholar 

  • Bernstein, S. (1927). Theory of Probability, Nauka, Moscow.

    Google Scholar 

  • Bhattacharya, R.N. and Rao, R.R. (1986). Normal Approximation and Asymptotic Expansions, Robert E. Krieger, Melbourne, FL.

    MATH  Google Scholar 

  • Bhattacharya, R.N. and Waymire, E. (2009). A Basic Course in Probability Theory, Springer, New York.

    Google Scholar 

  • Billingsley, P.(1995). Probability and Measure, Third Edition, John Wiley, New York.

    MATH  Google Scholar 

  • Breiman, L. (1992). Probability, Addison-Wesley, New York.

    Book  MATH  Google Scholar 

  • Brown, L., Cai, T., and DasGupta, A. (2001). Interval estimation for a binomial proportion, Statist. Sci., 16, 101–133.

    MathSciNet  MATH  Google Scholar 

  • Brown, L., Cai, T., and DasGupta, A. (2002). Confidence intervals for a binomial proportion and asymptotic expansions, Ann. Statist., 30, 160–201.

    Article  MathSciNet  MATH  Google Scholar 

  • Chernoff, H. (1952). A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations, Ann. Math. Statist., 23, 493–507.

    Article  MathSciNet  MATH  Google Scholar 

  • Chernoff, H. (1981). A note on an inequality involving the normal distribution, Ann. Prob., 9, 533–535.

    Article  MathSciNet  MATH  Google Scholar 

  • Chung, K. L. (1974). A Course in Probability, Academic Press, New York.

    MATH  Google Scholar 

  • DasGupta, A. (2008). Asymptotic Theory of Statistics and Probability, Springer, New York.

    MATH  Google Scholar 

  • DasGupta, A. (2010). Fundamentals of Probability: A First Course, Springer, New York.

    Book  MATH  Google Scholar 

  • Diaconis, P. and Zabell, S. (1991). Closed form summation formulae for classical distributions, Statist. Sci., 6, 284–302.

    Article  MathSciNet  MATH  Google Scholar 

  • Dudley, R. (2002). Real Analysis and Probability, Cambridge University Press, Cambridge, UK.

    Google Scholar 

  • Everitt, B. (1998). Cambridge Dictionary of Statistics, Cambridge University Press, New York.

    MATH  Google Scholar 

  • Feller, W. (1968). Introduction to Probability Theory and its Applications, Vol. I, Wiley, New York.

    MATH  Google Scholar 

  • Feller, W. (1971). Introduction to Probability Theory and Its Applications, Vol. II, Wiley, New York.

    MATH  Google Scholar 

  • Fisher, R.A. (1929). Moments and product moments of sampling distributions, Proc. London Math. Soc., 2, 199–238.

    Google Scholar 

  • Hall, P. (1992). The Bootstrap and Edgeworth Expansion, Springer-Verlag, New York.

    Google Scholar 

  • Johnson, N., Kotz, S., and Balakrishnan, N. (1994). Continuous Univariate Distributions, Vol. I, Wiley, New York.

    MATH  Google Scholar 

  • Kagan, A., Linnik, Y., and Rao, C.R. (1973). Characterization Problems in Mathematical Statistics, Wiley, New York.

    MATH  Google Scholar 

  • Kendall, M.G. and Stuart, A. (1976). Advanced Theory of Statistics, Vol. I, Wiley, New York.

    MATH  Google Scholar 

  • Le Cam, L. (1960). An approximation theorem for the Poisson binomial distribution, Pacific J. Math., 10, 1181–1197.

    Article  MathSciNet  MATH  Google Scholar 

  • Le Cam, L. (1986). The central limit theorem around 1935, Statist. Sci., 1, 78–96.

    Article  MathSciNet  Google Scholar 

  • Paley, R.E. and Zygmund, A. (1932). A note on analytic functions in the unit circle, Proc. Camb. Philos. Soc., 28, 266–272.

    Article  Google Scholar 

  • Petrov, V. (1975). Limit Theorems of Probability Theory, Oxford University Press, Oxford, UK.

    Google Scholar 

  • Pitman, J. (1992). Probability, Springer-Verlag, New York.

    Google Scholar 

  • Rao, C.R. (1973), Linear Statistical Inference and Applications, Wiley, New York.

    Book  MATH  Google Scholar 

  • Ross, S. (1984). A First Course in Probability, Macmillan, New York.

    MATH  Google Scholar 

  • Steele, J.M. (1994). Le Cam’s inequality and Poisson approximations, Amer. Math Month., 101, 48–54.

    Article  MathSciNet  MATH  Google Scholar 

  • Stein, C. (1981). Estimation of the mean of a multivariate normal distribution, Ann. Stat., 9, 1135–1151.

    Article  MATH  Google Scholar 

  • Stigler, S. (1986). The History of Statistics, Belknap Press, Cambridge, MA.

    MATH  Google Scholar 

  • Stirzaker, D. (1994). Elementary Probability, Cambridge University Press, London.

    MATH  Google Scholar 

  • Wasserman, L. (2006). All of Nonparametric Statistics, Springer, New York.

    MATH  Google Scholar 

  • Widder, D. (1989). Advanced Calculus, Dover, New York.

    MATH  Google Scholar 

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Correspondence to Anirban DasGupta .

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DasGupta, A. (2011). Review of Univariate Probability. In: Probability for Statistics and Machine Learning. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9634-3_1

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