Abstract
The basic probability concepts are introduced in this chapter. The definitions of probability along with experiment, sample space, event, equally likely, and mutually exclusive outcomes are highlighted. The important operations on events are discussed with examples. The concepts of marginal and conditional probabilities are discussed in a self-explanatory manner with several examples. The multiplication rules of probability and the concept of independent events are introduced in this chapter. The applications of conditional probability and Bayes’ theorem in analyzing epidemiological data are shown. The measures of sensitivity and specificity of tests are illustrated with examples.
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Reference
Mangasarian, O. L., Setiono, R., & Wolberg, W. H. (1990). Pattern recognition via linear programming: Theory and application to medical diagnosis. In T. F. Coleman & Y. Li (Eds.), Large-scale numerical optimization (pp. 22–30). Philadelphia: SIAM Publications.
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Islam, M.A., Al-Shiha, A. (2018). Basic Probability Concepts. In: Foundations of Biostatistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-8627-4_3
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DOI: https://doi.org/10.1007/978-981-10-8627-4_3
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Online ISBN: 978-981-10-8627-4
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