L14.4 The Bayesian Inference Framework | Summary and Q&A

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April 24, 2018
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L14.4 The Bayesian Inference Framework

TL;DR

Bayesian Inference is a framework that treats unknown variables as random and uses prior beliefs and data observations to calculate conditional distributions.

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Q: How is Bayesian inference different from other frameworks?

In Bayesian inference, unknown variables are treated as random variables with prior distributions, while other frameworks consider them as unknown constants.

Q: What is the role of the prior distribution in Bayesian inference?

The prior distribution represents prior beliefs about the unknown variable before any data is obtained. It serves as a starting point for the inference process.

Q: How is the observation process modeled in Bayesian inference?

The observation process is modeled using a probabilistic model, specifying the conditional distribution of the observations given specific values of the unknown variable.

Q: How is the posterior distribution calculated in Bayesian inference?

Once specific observations are obtained, Bayes' rule is used to calculate the conditional distribution of the unknown variable (Theta). This provides a complete solution to the Bayesian inference problem.

Summary & Key Takeaways

• The Bayesian inference framework treats unknown variables as random, with a prior distribution representing prior beliefs before data is obtained.

• Data observations are modeled using a probabilistic model, specifying the conditional distribution of the observations given specific values of the unknown variable.

• Using Bayes' rule, the conditional distribution of the unknown variable (Theta) can be calculated, providing a complete solution to the Bayesian inference problem.