How can we use Bayesian statistics to analyze the probability of events when we have prior knowledge about related parameters?

Bayesian statistics allows us to update the probability of an event based on prior knowledge and new data. We start with a prior distribution representing our initial beliefs about parameters. As new data becomes available, we use Bayes’ theorem to compute the posterior distribution, which combines prior beliefs and evidence. This updated distribution provides a refined probability assessment, aiding in more accurate predictions and decision-making.

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