Lecture 5

Question 1

Sample Space (Ω)

Answer 1

The set of all possible worlds (in terms of probability theory)

Question 2

Random Variable (probability theory)

Answer 2

A function that maps from the domain of possible worlds Ω to some range - the set of possible values it can take on.

E.g., Total for two dice is the set {2…12}

NOTE: Random varaibles begin with an Uppercase letter.

Question 3

What is the Markov Assumption?

Answer 3

The current state depends only on a finite fixed number of previous states.

Question 4

Transition Model

Answer 4

Specifies probability distribution over the latest state variables, given all previous values.

Question 5

Sensor Model

Answer 5

How the evidence variables get their values.

Using Sensor Markov Assumption:
- Any E_t should suffice to generate the current sensor values
P(E_t | X_t)