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PDF A Bayesian reasoning refers to a structured approach in decision-making and inference that uses Bayes' theorem to update the probability of a hypothesis as more evidence or information becomes available.
Individuals or entities engaged in statistical modeling, data analysis, or decision-making scenarios that require probabilistic inference may find it necessary to apply Bayesian reasoning.
To fill out a Bayesian reasoning model, one must specify prior probabilities, likelihoods based on evidence, and compute posterior probabilities using Bayes' theorem.
The purpose of Bayesian reasoning is to provide a coherent method for updating beliefs in light of new evidence, facilitating better decision-making in uncertain situations.
Information that needs to be reported includes prior distributions, likelihood functions, observed data, and the resulting posterior distributions.
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