c13 quantifying uncertainty

Question 1

prior probabilities or marginal probabilities

Answer 1

Question 2

conditional probabilities

Answer 2

Question 3

axioms of probability

Question 4

full joint probability distribution

Answer 4

specifies the probability of each complete as- signment of values to random variables. It is usually too large to create or use in its explicit form

Question 5

Absolute independence

Answer 5

Absolute independence between subsets of random variables allows the full joint dis- tribution to be factored into smaller joint distributions, greatly reducing its complexity. Absolute independence seldom occurs in practice.

Question 6

Bayes’ rule

Answer 6

Question 7

Conditional independence

Answer 7

brought about by direct causal relationships in the domain might allow the full joint distribution to be factored into smaller, conditional distri- butions.

Question 8

naive Bayes

Answer 8

The naive Bayes model assumes the conditional independence of all effect variables, given a single cause variable, and grows linearly with the number of effects.