Bayesian Analysis
Bayesian analysis is a statistical approach that involves updating the probability of a hypothesis as new data becomes available. It is based on Bayes' theorem, which states that the probability of a hypothesis is proportional to the likelihood of the data given the hypothesis, multiplied by the prior probability of the hypothesis. Bayesian analysis allows for the incorporation of prior knowledge or beliefs about the hypothesis, which can be useful in situations where there is limited data or where the data is noisy. It also provides a framework for model selection and hypothesis testing, as well as uncertainty quantification through the use of posterior distributions. Bayesian analysis has applications in a wide range of fields, including machine learning, natural language processing, and image analysis.
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