### Reading Questions for Lecture 6

Parametric Methods (Ch. 4.1-4.5)

- Why do we compute the log likelihood of the data instead of just computing the likelihood?
- What is the "parameter" required to specify a Bernoulli distribution?
- If I flip a coin and get { heads, tails, tails, heads, tails }, what is the maximum likelihood estimate of the probability of getting heads?
- What two parameters are needed to specify a Gaussian distribution?
- The Bayes Estimator is a weighted average of what two values?
**(Robert)**Do you always want to select an unbiased estimator over a biased estimator?**(Deidre)**Can we use MAP or the Bayes Estimator as a tool for validation or verification?**(Roice)**How can the sample average "m" be considered an unbiased estimator of the mean "μ" unless it has an infinite number of samples?**(Roice)**Does "bias(d) = E[d(X)] - θ" have the same meaning as "E[m] = E[Σ x^{t}/N] = μ"?