课程: Probability Foundations for Data Science

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Bayesian inference

Bayesian inference

课程: Probability Foundations for Data Science

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Bayesian inference

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- [Instructor] Let's explore Bayesian inference. Bayesian inference is a method in probability where Bayes's theorem is used to update the probability for a hypothesis as more evidence or information becomes available. This approach combines prior knowledge, also known as prior probabilities, with new data, also known as the likelihood, to form an updated posterior probability. Let's take a moment to review the key aspects of Bayes's formula. Remember, the posterior probability is the updated probability of the hypothesis A after observing the data B. This is the result of Bayesian updating. The likelihood is the probability of observing the data B given that the hypothesis A is true. This measures how well the hypothesis explains the observed data. The prior probability is the probability of the hypothesis A before observing the current data. This represents the initial belief about the hypothesis. Finally, there is the marginal likelihood, which is the total probability of observing…

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