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- W4230487027 abstract "Statistical methods such as those discussed earlier in this text are familiar to a wide range of scientists; somewhat less familiar is the other school of statistics – Bayesian statistics. Bayesian statistics, based on the work of Reverend Thomas Bayes, introduces the concept of conditional probabilities, whereby we are interested in the “probability of A given B.” But Bayesian reasoning and the associated statistical methods go further than this. In clinical settings, using tests for purposes such as determining the probability of a woman having breast cancer, given a positive mammography versus a woman having a positive mammography who does not have breast cancer is an important consideration; conditional probabilities, a staple of Bayesian statistics, provides a robust tool for such an investigation. In this chapter, we consider the fundamental theory of Bayesian statistics, including Bayes’ theorem and formulations, probabilities, odds ratios, Bayes factors, and the like, and consider an example where we look at using Bayesian techniques for simple classification of proteins as intracellular or extracellular. We then consider Bayesian reasoning and Bayesian classification a little more formally, following this by considering a widely used classifier – the naïve Bayes classifier – using both the R language environment and an open source data mining tool: Weka. Bayesian belief networks provide an important tool in allowing us to represent dependencies between attributes, as well as classification, and we consider that next, along with methods for estimating parameters and then how the concept of priors and posteriors applies in the multiple dataset scenario." @default.
- W4230487027 created "2022-05-11" @default.
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- W4230487027 date "2011-11-12" @default.
- W4230487027 modified "2023-10-18" @default.
- W4230487027 title "Bayesian Statistics" @default.
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- W4230487027 doi "https://doi.org/10.1007/978-1-59745-290-8_7" @default.
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