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Questions and Answers
What is the likelihood function?
What is the likelihood function?
- The joint probability of observed data viewed as a function of the parameters of a statistical model (correct)
- The posterior probability in Bayesian statistics
- The probability density of observed data
- The arg max over the parameter in maximum likelihood estimation
In maximum likelihood estimation, what serves as a point estimate for the parameter?
In maximum likelihood estimation, what serves as a point estimate for the parameter?
- The Fisher information
- Arg max of the likelihood function (correct)
- The posterior probability
- The Hessian matrix of the likelihood function
What does the Fisher information indicate in maximum likelihood estimation?
What does the Fisher information indicate in maximum likelihood estimation?
- The estimate's precision (correct)
- The estimate's consistency
- The estimate's bias
- The estimate's accuracy
How are parameter estimates derived in Bayesian statistics?
How are parameter estimates derived in Bayesian statistics?
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How is the likelihood function parameterized?
How is the likelihood function parameterized?
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