Lecture 2 – Combining Sources of Evidence

Question 1

Equation for posterior probability and define

Answer 1

Pr (H | D ) - The probability of the thing you’re wondering about given what you know.

Question 2

Equation for prior probability and define

Answer 2

(Pr(H) - prior to having the data/extra knowledge

Question 3

Define this equation Pr ( D | ¬H )

Answer 3

the probability of thing I know to be true if thing I’m wondering about were NOT true. (¬H = hypothesis not true).

Question 4

Bayes Theorem

Answer 4

This is a mathematical formula that determines probability. Pr(H ∣ D) = Pr(D ∣ H)Pr(H) / Pr(D ∣ H)Pr(H) + Pr(D ∣ ¬H)Pr(¬H).

Question 5

What is this: Pr(H ∣ D) = Pr(D ∣ H)Pr(H) / Pr(D ∣ H)Pr(H) + Pr(D ∣ ¬H)Pr(¬H)

Answer 5

Bayes Theorem

Question 6

Whats the distinction between the normative and descriptive use of Bayes’ Theorem?

Answer 6

• Normative: how we should reason

- Descriptive: how we actually reason